Shortcuts

Chatbot Tutorial

Author: Matthew Inkawhich In this tutorial, we explore a fun and interesting use-case of recurrent sequence-to-sequence models. We will train a simple chatbot using movie scripts from the Cornell Movie-Dialogs Corpus.

Conversational models are a hot topic in artificial intelligence research. Chatbots can be found in a variety of settings, including customer service applications and online helpdesks. These bots are often powered by retrieval-based models, which output predefined responses to questions of certain forms. In a highly restricted domain like a company’s IT helpdesk, these models may be sufficient, however, they are not robust enough for more general use-cases. Teaching a machine to carry out a meaningful conversation with a human in multiple domains is a research question that is far from solved. Recently, the deep learning boom has allowed for powerful generative models like Google’s Neural Conversational Model, which marks a large step towards multi-domain generative conversational models. In this tutorial, we will implement this kind of model in PyTorch.

bot
> hello?
Bot: hello .
> where am I?
Bot: you re in a hospital .
> who are you?
Bot: i m a lawyer .
> how are you doing?
Bot: i m fine .
> are you my friend?
Bot: no .
> you're under arrest
Bot: i m trying to help you !
> i'm just kidding
Bot: i m sorry .
> where are you from?
Bot: san francisco .
> it's time for me to leave
Bot: i know .
> goodbye
Bot: goodbye .

Tutorial Highlights

Acknowledgements

This tutorial borrows code from the following sources:

  1. Yuan-Kuei Wu’s pytorch-chatbot implementation: https://github.com/ywk991112/pytorch-chatbot
  2. Sean Robertson’s practical-pytorch seq2seq-translation example: https://github.com/spro/practical-pytorch/tree/master/seq2seq-translation
  3. FloydHub’s Cornell Movie Corpus preprocessing code: https://github.com/floydhub/textutil-preprocess-cornell-movie-corpus

Preparations

To start, Download the data ZIP file here and put in a data/ directory under the current directory.

After that, let’s import some necessities.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import torch
from torch.jit import script, trace
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import csv
import random
import re
import os
import unicodedata
import codecs
from io import open
import itertools
import math


USE_CUDA = torch.cuda.is_available()
device = torch.device("cuda" if USE_CUDA else "cpu")

Load & Preprocess Data

The next step is to reformat our data file and load the data into structures that we can work with.

The Cornell Movie-Dialogs Corpus is a rich dataset of movie character dialog:

  • 220,579 conversational exchanges between 10,292 pairs of movie characters
  • 9,035 characters from 617 movies
  • 304,713 total utterances

This dataset is large and diverse, and there is a great variation of language formality, time periods, sentiment, etc. Our hope is that this diversity makes our model robust to many forms of inputs and queries.

First, we’ll take a look at some lines of our datafile to see the original format.

corpus_name = "cornell movie-dialogs corpus"
corpus = os.path.join("data", corpus_name)

def printLines(file, n=10):
    with open(file, 'rb') as datafile:
        lines = datafile.readlines()
    for line in lines[:n]:
        print(line)

printLines(os.path.join(corpus, "movie_lines.txt"))

Out:

b'L1045 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ They do not!\n'
b'L1044 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ They do to!\n'
b'L985 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ I hope so.\n'
b'L984 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ She okay?\n'
b"L925 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ Let's go.\n"
b'L924 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ Wow\n'
b"L872 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ Okay -- you're gonna need to learn how to lie.\n"
b'L871 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ No\n'
b'L870 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ I\'m kidding.  You know how sometimes you just become this "persona"?  And you don\'t know how to quit?\n'
b'L869 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ Like my fear of wearing pastels?\n'

Create formatted data file

For convenience, we’ll create a nicely formatted data file in which each line contains a tab-separated query sentence and a response sentence pair.

The following functions facilitate the parsing of the raw movie_lines.txt data file.

  • loadLines splits each line of the file into a dictionary of fields (lineID, characterID, movieID, character, text)
  • loadConversations groups fields of lines from loadLines into conversations based on movie_conversations.txt
  • extractSentencePairs extracts pairs of sentences from conversations
# Splits each line of the file into a dictionary of fields
def loadLines(fileName, fields):
    lines = {}
    with open(fileName, 'r', encoding='iso-8859-1') as f:
        for line in f:
            values = line.split(" +++$+++ ")
            # Extract fields
            lineObj = {}
            for i, field in enumerate(fields):
                lineObj[field] = values[i]
            lines[lineObj['lineID']] = lineObj
    return lines


# Groups fields of lines from `loadLines` into conversations based on *movie_conversations.txt*
def loadConversations(fileName, lines, fields):
    conversations = []
    with open(fileName, 'r', encoding='iso-8859-1') as f:
        for line in f:
            values = line.split(" +++$+++ ")
            # Extract fields
            convObj = {}
            for i, field in enumerate(fields):
                convObj[field] = values[i]
            # Convert string to list (convObj["utteranceIDs"] == "['L598485', 'L598486', ...]")
            utterance_id_pattern = re.compile('L[0-9]+')
            lineIds = utterance_id_pattern.findall(convObj["utteranceIDs"])
            # Reassemble lines
            convObj["lines"] = []
            for lineId in lineIds:
                convObj["lines"].append(lines[lineId])
            conversations.append(convObj)
    return conversations


# Extracts pairs of sentences from conversations
def extractSentencePairs(conversations):
    qa_pairs = []
    for conversation in conversations:
        # Iterate over all the lines of the conversation
        for i in range(len(conversation["lines"]) - 1):  # We ignore the last line (no answer for it)
            inputLine = conversation["lines"][i]["text"].strip()
            targetLine = conversation["lines"][i+1]["text"].strip()
            # Filter wrong samples (if one of the lists is empty)
            if inputLine and targetLine:
                qa_pairs.append([inputLine, targetLine])
    return qa_pairs

Now we’ll call these functions and create the file. We’ll call it formatted_movie_lines.txt.

# Define path to new file
datafile = os.path.join(corpus, "formatted_movie_lines.txt")

delimiter = '\t'
# Unescape the delimiter
delimiter = str(codecs.decode(delimiter, "unicode_escape"))

# Initialize lines dict, conversations list, and field ids
lines = {}
conversations = []
MOVIE_LINES_FIELDS = ["lineID", "characterID", "movieID", "character", "text"]
MOVIE_CONVERSATIONS_FIELDS = ["character1ID", "character2ID", "movieID", "utteranceIDs"]

# Load lines and process conversations
print("\nProcessing corpus...")
lines = loadLines(os.path.join(corpus, "movie_lines.txt"), MOVIE_LINES_FIELDS)
print("\nLoading conversations...")
conversations = loadConversations(os.path.join(corpus, "movie_conversations.txt"),
                                  lines, MOVIE_CONVERSATIONS_FIELDS)

# Write new csv file
print("\nWriting newly formatted file...")
with open(datafile, 'w', encoding='utf-8') as outputfile:
    writer = csv.writer(outputfile, delimiter=delimiter, lineterminator='\n')
    for pair in extractSentencePairs(conversations):
        writer.writerow(pair)

# Print a sample of lines
print("\nSample lines from file:")
printLines(datafile)

Out:

Processing corpus...

Loading conversations...

Writing newly formatted file...

Sample lines from file:
b"Can we make this quick?  Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad.  Again.\tWell, I thought we'd start with pronunciation, if that's okay with you.\n"
b"Well, I thought we'd start with pronunciation, if that's okay with you.\tNot the hacking and gagging and spitting part.  Please.\n"
b"Not the hacking and gagging and spitting part.  Please.\tOkay... then how 'bout we try out some French cuisine.  Saturday?  Night?\n"
b"You're asking me out.  That's so cute. What's your name again?\tForget it.\n"
b"No, no, it's my fault -- we didn't have a proper introduction ---\tCameron.\n"
b"Cameron.\tThe thing is, Cameron -- I'm at the mercy of a particularly hideous breed of loser.  My sister.  I can't date until she does.\n"
b"The thing is, Cameron -- I'm at the mercy of a particularly hideous breed of loser.  My sister.  I can't date until she does.\tSeems like she could get a date easy enough...\n"
b'Why?\tUnsolved mystery.  She used to be really popular when she started high school, then it was just like she got sick of it or something.\n'
b"Unsolved mystery.  She used to be really popular when she started high school, then it was just like she got sick of it or something.\tThat's a shame.\n"
b'Gosh, if only we could find Kat a boyfriend...\tLet me see what I can do.\n'

Load and trim data

Our next order of business is to create a vocabulary and load query/response sentence pairs into memory.

Note that we are dealing with sequences of words, which do not have an implicit mapping to a discrete numerical space. Thus, we must create one by mapping each unique word that we encounter in our dataset to an index value.

For this we define a Voc class, which keeps a mapping from words to indexes, a reverse mapping of indexes to words, a count of each word and a total word count. The class provides methods for adding a word to the vocabulary (addWord), adding all words in a sentence (addSentence) and trimming infrequently seen words (trim). More on trimming later.

# Default word tokens
PAD_token = 0  # Used for padding short sentences
SOS_token = 1  # Start-of-sentence token
EOS_token = 2  # End-of-sentence token

class Voc:
    def __init__(self, name):
        self.name = name
        self.trimmed = False
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3  # Count SOS, EOS, PAD

    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.num_words
            self.word2count[word] = 1
            self.index2word[self.num_words] = word
            self.num_words += 1
        else:
            self.word2count[word] += 1

    # Remove words below a certain count threshold
    def trim(self, min_count):
        if self.trimmed:
            return
        self.trimmed = True

        keep_words = []

        for k, v in self.word2count.items():
            if v >= min_count:
                keep_words.append(k)

        print('keep_words {} / {} = {:.4f}'.format(
            len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index)
        ))

        # Reinitialize dictionaries
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3 # Count default tokens

        for word in keep_words:
            self.addWord(word)

Now we can assemble our vocabulary and query/response sentence pairs. Before we are ready to use this data, we must perform some preprocessing.

First, we must convert the Unicode strings to ASCII using unicodeToAscii. Next, we should convert all letters to lowercase and trim all non-letter characters except for basic punctuation (normalizeString). Finally, to aid in training convergence, we will filter out sentences with length greater than the MAX_LENGTH threshold (filterPairs).

MAX_LENGTH = 10  # Maximum sentence length to consider

# Turn a Unicode string to plain ASCII, thanks to
# https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
    )

# Lowercase, trim, and remove non-letter characters
def normalizeString(s):
    s = unicodeToAscii(s.lower().strip())
    s = re.sub(r"([.!?])", r" \1", s)
    s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
    s = re.sub(r"\s+", r" ", s).strip()
    return s

# Read query/response pairs and return a voc object
def readVocs(datafile, corpus_name):
    print("Reading lines...")
    # Read the file and split into lines
    lines = open(datafile, encoding='utf-8').\
        read().strip().split('\n')
    # Split every line into pairs and normalize
    pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]
    voc = Voc(corpus_name)
    return voc, pairs

# Returns True iff both sentences in a pair 'p' are under the MAX_LENGTH threshold
def filterPair(p):
    # Input sequences need to preserve the last word for EOS token
    return len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH

# Filter pairs using filterPair condition
def filterPairs(pairs):
    return [pair for pair in pairs if filterPair(pair)]

# Using the functions defined above, return a populated voc object and pairs list
def loadPrepareData(corpus, corpus_name, datafile, save_dir):
    print("Start preparing training data ...")
    voc, pairs = readVocs(datafile, corpus_name)
    print("Read {!s} sentence pairs".format(len(pairs)))
    pairs = filterPairs(pairs)
    print("Trimmed to {!s} sentence pairs".format(len(pairs)))
    print("Counting words...")
    for pair in pairs:
        voc.addSentence(pair[0])
        voc.addSentence(pair[1])
    print("Counted words:", voc.num_words)
    return voc, pairs


# Load/Assemble voc and pairs
save_dir = os.path.join("data", "save")
voc, pairs = loadPrepareData(corpus, corpus_name, datafile, save_dir)
# Print some pairs to validate
print("\npairs:")
for pair in pairs[:10]:
    print(pair)

Out:

Start preparing training data ...
Reading lines...
Read 221282 sentence pairs
Trimmed to 64271 sentence pairs
Counting words...
Counted words: 18008

pairs:
['there .', 'where ?']
['you have my word . as a gentleman', 'you re sweet .']
['hi .', 'looks like things worked out tonight huh ?']
['you know chastity ?', 'i believe we share an art instructor']
['have fun tonight ?', 'tons']
['well no . . .', 'then that s all you had to say .']
['then that s all you had to say .', 'but']
['but', 'you always been this selfish ?']
['do you listen to this crap ?', 'what crap ?']
['what good stuff ?', 'the real you .']

Another tactic that is beneficial to achieving faster convergence during training is trimming rarely used words out of our vocabulary. Decreasing the feature space will also soften the difficulty of the function that the model must learn to approximate. We will do this as a two-step process:

  1. Trim words used under MIN_COUNT threshold using the voc.trim function.
  2. Filter out pairs with trimmed words.
MIN_COUNT = 3    # Minimum word count threshold for trimming

def trimRareWords(voc, pairs, MIN_COUNT):
    # Trim words used under the MIN_COUNT from the voc
    voc.trim(MIN_COUNT)
    # Filter out pairs with trimmed words
    keep_pairs = []
    for pair in pairs:
        input_sentence = pair[0]
        output_sentence = pair[1]
        keep_input = True
        keep_output = True
        # Check input sentence
        for word in input_sentence.split(' '):
            if word not in voc.word2index:
                keep_input = False
                break
        # Check output sentence
        for word in output_sentence.split(' '):
            if word not in voc.word2index:
                keep_output = False
                break

        # Only keep pairs that do not contain trimmed word(s) in their input or output sentence
        if keep_input and keep_output:
            keep_pairs.append(pair)

    print("Trimmed from {} pairs to {}, {:.4f} of total".format(len(pairs), len(keep_pairs), len(keep_pairs) / len(pairs)))
    return keep_pairs


# Trim voc and pairs
pairs = trimRareWords(voc, pairs, MIN_COUNT)

Out:

keep_words 7823 / 18005 = 0.4345
Trimmed from 64271 pairs to 53165, 0.8272 of total

Prepare Data for Models

Although we have put a great deal of effort into preparing and massaging our data into a nice vocabulary object and list of sentence pairs, our models will ultimately expect numerical torch tensors as inputs. One way to prepare the processed data for the models can be found in the seq2seq translation tutorial. In that tutorial, we use a batch size of 1, meaning that all we have to do is convert the words in our sentence pairs to their corresponding indexes from the vocabulary and feed this to the models.

However, if you’re interested in speeding up training and/or would like to leverage GPU parallelization capabilities, you will need to train with mini-batches.

Using mini-batches also means that we must be mindful of the variation of sentence length in our batches. To accommodate sentences of different sizes in the same batch, we will make our batched input tensor of shape (max_length, batch_size), where sentences shorter than the max_length are zero padded after an EOS_token.

If we simply convert our English sentences to tensors by converting words to their indexes(indexesFromSentence) and zero-pad, our tensor would have shape (batch_size, max_length) and indexing the first dimension would return a full sequence across all time-steps. However, we need to be able to index our batch along time, and across all sequences in the batch. Therefore, we transpose our input batch shape to (max_length, batch_size), so that indexing across the first dimension returns a time step across all sentences in the batch. We handle this transpose implicitly in the zeroPadding function.

batches

The inputVar function handles the process of converting sentences to tensor, ultimately creating a correctly shaped zero-padded tensor. It also returns a tensor of lengths for each of the sequences in the batch which will be passed to our decoder later.

The outputVar function performs a similar function to inputVar, but instead of returning a lengths tensor, it returns a binary mask tensor and a maximum target sentence length. The binary mask tensor has the same shape as the output target tensor, but every element that is a PAD_token is 0 and all others are 1.

batch2TrainData simply takes a bunch of pairs and returns the input and target tensors using the aforementioned functions.

def indexesFromSentence(voc, sentence):
    return [voc.word2index[word] for word in sentence.split(' ')] + [EOS_token]


def zeroPadding(l, fillvalue=PAD_token):
    return list(itertools.zip_longest(*l, fillvalue=fillvalue))

def binaryMatrix(l, value=PAD_token):
    m = []
    for i, seq in enumerate(l):
        m.append([])
        for token in seq:
            if token == PAD_token:
                m[i].append(0)
            else:
                m[i].append(1)
    return m

# Returns padded input sequence tensor and lengths
def inputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    padVar = torch.LongTensor(padList)
    return padVar, lengths

# Returns padded target sequence tensor, padding mask, and max target length
def outputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    max_target_len = max([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    mask = binaryMatrix(padList)
    mask = torch.BoolTensor(mask)
    padVar = torch.LongTensor(padList)
    return padVar, mask, max_target_len

# Returns all items for a given batch of pairs
def batch2TrainData(voc, pair_batch):
    pair_batch.sort(key=lambda x: len(x[0].split(" ")), reverse=True)
    input_batch, output_batch = [], []
    for pair in pair_batch:
        input_batch.append(pair[0])
        output_batch.append(pair[1])
    inp, lengths = inputVar(input_batch, voc)
    output, mask, max_target_len = outputVar(output_batch, voc)
    return inp, lengths, output, mask, max_target_len


# Example for validation
small_batch_size = 5
batches = batch2TrainData(voc, [random.choice(pairs) for _ in range(small_batch_size)])
input_variable, lengths, target_variable, mask, max_target_len = batches

print("input_variable:", input_variable)
print("lengths:", lengths)
print("target_variable:", target_variable)
print("mask:", mask)
print("max_target_len:", max_target_len)

Out:

input_variable: tensor([[  27,   25,   76,  158,   62],
        [  14,  200,  102,   21,    4],
        [ 123,   33,   98,   56,    2],
        [  40,  383,    9,  159,    0],
        [1589,    7, 1036,    4,    0],
        [   4,    4,    4,    2,    0],
        [   2,    2,    2,    0,    0]])
lengths: tensor([7, 7, 7, 6, 3])
target_variable: tensor([[   7, 1530,   50,   76,   64],
        [  94,  364, 1036,   37,    7],
        [ 117,  144,    6,  659, 2075],
        [  24,  139,   40,   36, 1235],
        [  36, 1494,  390,  306,   60],
        [  98,    4,    6,    6,   40],
        [ 192,    2,    2,    2,  155],
        [   4,    0,    0,    0,  159],
        [   2,    0,    0,    0,    6],
        [   0,    0,    0,    0,    2]])
mask: tensor([[ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True, False, False, False,  True],
        [ True, False, False, False,  True],
        [False, False, False, False,  True]])
max_target_len: 10

Define Models

Seq2Seq Model

The brains of our chatbot is a sequence-to-sequence (seq2seq) model. The goal of a seq2seq model is to take a variable-length sequence as an input, and return a variable-length sequence as an output using a fixed-sized model.

Sutskever et al. discovered that by using two separate recurrent neural nets together, we can accomplish this task. One RNN acts as an encoder, which encodes a variable length input sequence to a fixed-length context vector. In theory, this context vector (the final hidden layer of the RNN) will contain semantic information about the query sentence that is input to the bot. The second RNN is a decoder, which takes an input word and the context vector, and returns a guess for the next word in the sequence and a hidden state to use in the next iteration.

model

Image source: https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/

Encoder

The encoder RNN iterates through the input sentence one token (e.g. word) at a time, at each time step outputting an “output” vector and a “hidden state” vector. The hidden state vector is then passed to the next time step, while the output vector is recorded. The encoder transforms the context it saw at each point in the sequence into a set of points in a high-dimensional space, which the decoder will use to generate a meaningful output for the given task.

At the heart of our encoder is a multi-layered Gated Recurrent Unit, invented by Cho et al. in 2014. We will use a bidirectional variant of the GRU, meaning that there are essentially two independent RNNs: one that is fed the input sequence in normal sequential order, and one that is fed the input sequence in reverse order. The outputs of each network are summed at each time step. Using a bidirectional GRU will give us the advantage of encoding both past and future contexts.

Bidirectional RNN:

rnn_bidir

Image source: https://colah.github.io/posts/2015-09-NN-Types-FP/

Note that an embedding layer is used to encode our word indices in an arbitrarily sized feature space. For our models, this layer will map each word to a feature space of size hidden_size. When trained, these values should encode semantic similarity between similar meaning words.

Finally, if passing a padded batch of sequences to an RNN module, we must pack and unpack padding around the RNN pass using nn.utils.rnn.pack_padded_sequence and nn.utils.rnn.pad_packed_sequence respectively.

Computation Graph:

  1. Convert word indexes to embeddings.
  2. Pack padded batch of sequences for RNN module.
  3. Forward pass through GRU.
  4. Unpack padding.
  5. Sum bidirectional GRU outputs.
  6. Return output and final hidden state.

Inputs:

  • input_seq: batch of input sentences; shape=(max_length, batch_size)
  • input_lengths: list of sentence lengths corresponding to each sentence in the batch; shape=(batch_size)
  • hidden: hidden state; shape=(n_layers x num_directions, batch_size, hidden_size)

Outputs:

  • outputs: output features from the last hidden layer of the GRU (sum of bidirectional outputs); shape=(max_length, batch_size, hidden_size)
  • hidden: updated hidden state from GRU; shape=(n_layers x num_directions, batch_size, hidden_size)
class EncoderRNN(nn.Module):
    def __init__(self, hidden_size, embedding, n_layers=1, dropout=0):
        super(EncoderRNN, self).__init__()
        self.n_layers = n_layers
        self.hidden_size = hidden_size
        self.embedding = embedding

        # Initialize GRU; the input_size and hidden_size params are both set to 'hidden_size'
        #   because our input size is a word embedding with number of features == hidden_size
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers,
                          dropout=(0 if n_layers == 1 else dropout), bidirectional=True)

    def forward(self, input_seq, input_lengths, hidden=None):
        # Convert word indexes to embeddings
        embedded = self.embedding(input_seq)
        # Pack padded batch of sequences for RNN module
        packed = nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
        # Forward pass through GRU
        outputs, hidden = self.gru(packed, hidden)
        # Unpack padding
        outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs)
        # Sum bidirectional GRU outputs
        outputs = outputs[:, :, :self.hidden_size] + outputs[:, : ,self.hidden_size:]
        # Return output and final hidden state
        return outputs, hidden

Decoder

The decoder RNN generates the response sentence in a token-by-token fashion. It uses the encoder’s context vectors, and internal hidden states to generate the next word in the sequence. It continues generating words until it outputs an EOS_token, representing the end of the sentence. A common problem with a vanilla seq2seq decoder is that if we rely solely on the context vector to encode the entire input sequence’s meaning, it is likely that we will have information loss. This is especially the case when dealing with long input sequences, greatly limiting the capability of our decoder.

To combat this, Bahdanau et al. created an “attention mechanism” that allows the decoder to pay attention to certain parts of the input sequence, rather than using the entire fixed context at every step.

At a high level, attention is calculated using the decoder’s current hidden state and the encoder’s outputs. The output attention weights have the same shape as the input sequence, allowing us to multiply them by the encoder outputs, giving us a weighted sum which indicates the parts of encoder output to pay attention to. Sean Robertson’s figure describes this very well:

attn2

Luong et al. improved upon Bahdanau et al.’s groundwork by creating “Global attention”. The key difference is that with “Global attention”, we consider all of the encoder’s hidden states, as opposed to Bahdanau et al.’s “Local attention”, which only considers the encoder’s hidden state from the current time step. Another difference is that with “Global attention”, we calculate attention weights, or energies, using the hidden state of the decoder from the current time step only. Bahdanau et al.’s attention calculation requires knowledge of the decoder’s state from the previous time step. Also, Luong et al. provides various methods to calculate the attention energies between the encoder output and decoder output which are called “score functions”:

scores

where \(h_t\) = current target decoder state and \(\bar{h}_s\) = all encoder states.

Overall, the Global attention mechanism can be summarized by the following figure. Note that we will implement the “Attention Layer” as a separate nn.Module called Attn. The output of this module is a softmax normalized weights tensor of shape (batch_size, 1, max_length).

global_attn
# Luong attention layer
class Attn(nn.Module):
    def __init__(self, method, hidden_size):
        super(Attn, self).__init__()
        self.method = method
        if self.method not in ['dot', 'general', 'concat']:
            raise ValueError(self.method, "is not an appropriate attention method.")
        self.hidden_size = hidden_size
        if self.method == 'general':
            self.attn = nn.Linear(self.hidden_size, hidden_size)
        elif self.method == 'concat':
            self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
            self.v = nn.Parameter(torch.FloatTensor(hidden_size))

    def dot_score(self, hidden, encoder_output):
        return torch.sum(hidden * encoder_output, dim=2)

    def general_score(self, hidden, encoder_output):
        energy = self.attn(encoder_output)
        return torch.sum(hidden * energy, dim=2)

    def concat_score(self, hidden, encoder_output):
        energy = self.attn(torch.cat((hidden.expand(encoder_output.size(0), -1, -1), encoder_output), 2)).tanh()
        return torch.sum(self.v * energy, dim=2)

    def forward(self, hidden, encoder_outputs):
        # Calculate the attention weights (energies) based on the given method
        if self.method == 'general':
            attn_energies = self.general_score(hidden, encoder_outputs)
        elif self.method == 'concat':
            attn_energies = self.concat_score(hidden, encoder_outputs)
        elif self.method == 'dot':
            attn_energies = self.dot_score(hidden, encoder_outputs)

        # Transpose max_length and batch_size dimensions
        attn_energies = attn_energies.t()

        # Return the softmax normalized probability scores (with added dimension)
        return F.softmax(attn_energies, dim=1).unsqueeze(1)

Now that we have defined our attention submodule, we can implement the actual decoder model. For the decoder, we will manually feed our batch one time step at a time. This means that our embedded word tensor and GRU output will both have shape (1, batch_size, hidden_size).

Computation Graph:

  1. Get embedding of current input word.
  2. Forward through unidirectional GRU.
  3. Calculate attention weights from the current GRU output from (2).
  4. Multiply attention weights to encoder outputs to get new “weighted sum” context vector.
  5. Concatenate weighted context vector and GRU output using Luong eq. 5.
  6. Predict next word using Luong eq. 6 (without softmax).
  7. Return output and final hidden state.

Inputs:

  • input_step: one time step (one word) of input sequence batch; shape=(1, batch_size)
  • last_hidden: final hidden layer of GRU; shape=(n_layers x num_directions, batch_size, hidden_size)
  • encoder_outputs: encoder model’s output; shape=(max_length, batch_size, hidden_size)

Outputs:

  • output: softmax normalized tensor giving probabilities of each word being the correct next word in the decoded sequence; shape=(batch_size, voc.num_words)
  • hidden: final hidden state of GRU; shape=(n_layers x num_directions, batch_size, hidden_size)
class LuongAttnDecoderRNN(nn.Module):
    def __init__(self, attn_model, embedding, hidden_size, output_size, n_layers=1, dropout=0.1):
        super(LuongAttnDecoderRNN, self).__init__()

        # Keep for reference
        self.attn_model = attn_model
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.n_layers = n_layers
        self.dropout = dropout

        # Define layers
        self.embedding = embedding
        self.embedding_dropout = nn.Dropout(dropout)
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers, dropout=(0 if n_layers == 1 else dropout))
        self.concat = nn.Linear(hidden_size * 2, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)

        self.attn = Attn(attn_model, hidden_size)

    def forward(self, input_step, last_hidden, encoder_outputs):
        # Note: we run this one step (word) at a time
        # Get embedding of current input word
        embedded = self.embedding(input_step)
        embedded = self.embedding_dropout(embedded)
        # Forward through unidirectional GRU
        rnn_output, hidden = self.gru(embedded, last_hidden)
        # Calculate attention weights from the current GRU output
        attn_weights = self.attn(rnn_output, encoder_outputs)
        # Multiply attention weights to encoder outputs to get new "weighted sum" context vector
        context = attn_weights.bmm(encoder_outputs.transpose(0, 1))
        # Concatenate weighted context vector and GRU output using Luong eq. 5
        rnn_output = rnn_output.squeeze(0)
        context = context.squeeze(1)
        concat_input = torch.cat((rnn_output, context), 1)
        concat_output = torch.tanh(self.concat(concat_input))
        # Predict next word using Luong eq. 6
        output = self.out(concat_output)
        output = F.softmax(output, dim=1)
        # Return output and final hidden state
        return output, hidden

Define Training Procedure

Masked loss

Since we are dealing with batches of padded sequences, we cannot simply consider all elements of the tensor when calculating loss. We define maskNLLLoss to calculate our loss based on our decoder’s output tensor, the target tensor, and a binary mask tensor describing the padding of the target tensor. This loss function calculates the average negative log likelihood of the elements that correspond to a 1 in the mask tensor.

def maskNLLLoss(inp, target, mask):
    nTotal = mask.sum()
    crossEntropy = -torch.log(torch.gather(inp, 1, target.view(-1, 1)).squeeze(1))
    loss = crossEntropy.masked_select(mask).mean()
    loss = loss.to(device)
    return loss, nTotal.item()

Single training iteration

The train function contains the algorithm for a single training iteration (a single batch of inputs).

We will use a couple of clever tricks to aid in convergence:

  • The first trick is using teacher forcing. This means that at some probability, set by teacher_forcing_ratio, we use the current target word as the decoder’s next input rather than using the decoder’s current guess. This technique acts as training wheels for the decoder, aiding in more efficient training. However, teacher forcing can lead to model instability during inference, as the decoder may not have a sufficient chance to truly craft its own output sequences during training. Thus, we must be mindful of how we are setting the teacher_forcing_ratio, and not be fooled by fast convergence.
  • The second trick that we implement is gradient clipping. This is a commonly used technique for countering the “exploding gradient” problem. In essence, by clipping or thresholding gradients to a maximum value, we prevent the gradients from growing exponentially and either overflow (NaN), or overshoot steep cliffs in the cost function.
grad_clip

Image source: Goodfellow et al. Deep Learning. 2016. https://www.deeplearningbook.org/

Sequence of Operations:

  1. Forward pass entire input batch through encoder.
  2. Initialize decoder inputs as SOS_token, and hidden state as the encoder’s final hidden state.
  3. Forward input batch sequence through decoder one time step at a time.
  4. If teacher forcing: set next decoder input as the current target; else: set next decoder input as current decoder output.
  5. Calculate and accumulate loss.
  6. Perform backpropagation.
  7. Clip gradients.
  8. Update encoder and decoder model parameters.

Note

PyTorch’s RNN modules (RNN, LSTM, GRU) can be used like any other non-recurrent layers by simply passing them the entire input sequence (or batch of sequences). We use the GRU layer like this in the encoder. The reality is that under the hood, there is an iterative process looping over each time step calculating hidden states. Alternatively, you can run these modules one time-step at a time. In this case, we manually loop over the sequences during the training process like we must do for the decoder model. As long as you maintain the correct conceptual model of these modules, implementing sequential models can be very straightforward.

def train(input_variable, lengths, target_variable, mask, max_target_len, encoder, decoder, embedding,
          encoder_optimizer, decoder_optimizer, batch_size, clip, max_length=MAX_LENGTH):

    # Zero gradients
    encoder_optimizer.zero_grad()
    decoder_optimizer.zero_grad()

    # Set device options
    input_variable = input_variable.to(device)
    target_variable = target_variable.to(device)
    mask = mask.to(device)
    # Lengths for rnn packing should always be on the cpu
    lengths = lengths.to("cpu")

    # Initialize variables
    loss = 0
    print_losses = []
    n_totals = 0

    # Forward pass through encoder
    encoder_outputs, encoder_hidden = encoder(input_variable, lengths)

    # Create initial decoder input (start with SOS tokens for each sentence)
    decoder_input = torch.LongTensor([[SOS_token for _ in range(batch_size)]])
    decoder_input = decoder_input.to(device)

    # Set initial decoder hidden state to the encoder's final hidden state
    decoder_hidden = encoder_hidden[:decoder.n_layers]

    # Determine if we are using teacher forcing this iteration
    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False

    # Forward batch of sequences through decoder one time step at a time
    if use_teacher_forcing:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # Teacher forcing: next input is current target
            decoder_input = target_variable[t].view(1, -1)
            # Calculate and accumulate loss
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal
    else:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # No teacher forcing: next input is decoder's own current output
            _, topi = decoder_output.topk(1)
            decoder_input = torch.LongTensor([[topi[i][0] for i in range(batch_size)]])
            decoder_input = decoder_input.to(device)
            # Calculate and accumulate loss
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal

    # Perform backpropatation
    loss.backward()

    # Clip gradients: gradients are modified in place
    _ = nn.utils.clip_grad_norm_(encoder.parameters(), clip)
    _ = nn.utils.clip_grad_norm_(decoder.parameters(), clip)

    # Adjust model weights
    encoder_optimizer.step()
    decoder_optimizer.step()

    return sum(print_losses) / n_totals

Training iterations

It is finally time to tie the full training procedure together with the data. The trainIters function is responsible for running n_iterations of training given the passed models, optimizers, data, etc. This function is quite self explanatory, as we have done the heavy lifting with the train function.

One thing to note is that when we save our model, we save a tarball containing the encoder and decoder state_dicts (parameters), the optimizers’ state_dicts, the loss, the iteration, etc. Saving the model in this way will give us the ultimate flexibility with the checkpoint. After loading a checkpoint, we will be able to use the model parameters to run inference, or we can continue training right where we left off.

def trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer, embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size, print_every, save_every, clip, corpus_name, loadFilename):

    # Load batches for each iteration
    training_batches = [batch2TrainData(voc, [random.choice(pairs) for _ in range(batch_size)])
                      for _ in range(n_iteration)]

    # Initializations
    print('Initializing ...')
    start_iteration = 1
    print_loss = 0
    if loadFilename:
        start_iteration = checkpoint['iteration'] + 1

    # Training loop
    print("Training...")
    for iteration in range(start_iteration, n_iteration + 1):
        training_batch = training_batches[iteration - 1]
        # Extract fields from batch
        input_variable, lengths, target_variable, mask, max_target_len = training_batch

        # Run a training iteration with batch
        loss = train(input_variable, lengths, target_variable, mask, max_target_len, encoder,
                     decoder, embedding, encoder_optimizer, decoder_optimizer, batch_size, clip)
        print_loss += loss

        # Print progress
        if iteration % print_every == 0:
            print_loss_avg = print_loss / print_every
            print("Iteration: {}; Percent complete: {:.1f}%; Average loss: {:.4f}".format(iteration, iteration / n_iteration * 100, print_loss_avg))
            print_loss = 0

        # Save checkpoint
        if (iteration % save_every == 0):
            directory = os.path.join(save_dir, model_name, corpus_name, '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size))
            if not os.path.exists(directory):
                os.makedirs(directory)
            torch.save({
                'iteration': iteration,
                'en': encoder.state_dict(),
                'de': decoder.state_dict(),
                'en_opt': encoder_optimizer.state_dict(),
                'de_opt': decoder_optimizer.state_dict(),
                'loss': loss,
                'voc_dict': voc.__dict__,
                'embedding': embedding.state_dict()
            }, os.path.join(directory, '{}_{}.tar'.format(iteration, 'checkpoint')))

Define Evaluation

After training a model, we want to be able to talk to the bot ourselves. First, we must define how we want the model to decode the encoded input.

Greedy decoding

Greedy decoding is the decoding method that we use during training when we are NOT using teacher forcing. In other words, for each time step, we simply choose the word from decoder_output with the highest softmax value. This decoding method is optimal on a single time-step level.

To facilitate the greedy decoding operation, we define a GreedySearchDecoder class. When run, an object of this class takes an input sequence (input_seq) of shape (input_seq length, 1), a scalar input length (input_length) tensor, and a max_length to bound the response sentence length. The input sentence is evaluated using the following computational graph:

Computation Graph:

  1. Forward input through encoder model.
  2. Prepare encoder’s final hidden layer to be first hidden input to the decoder.
  3. Initialize decoder’s first input as SOS_token.
  4. Initialize tensors to append decoded words to.
  5. Iteratively decode one word token at a time:
    1. Forward pass through decoder.
    2. Obtain most likely word token and its softmax score.
    3. Record token and score.
    4. Prepare current token to be next decoder input.
  6. Return collections of word tokens and scores.
class GreedySearchDecoder(nn.Module):
    def __init__(self, encoder, decoder):
        super(GreedySearchDecoder, self).__init__()
        self.encoder = encoder
        self.decoder = decoder

    def forward(self, input_seq, input_length, max_length):
        # Forward input through encoder model
        encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
        # Prepare encoder's final hidden layer to be first hidden input to the decoder
        decoder_hidden = encoder_hidden[:decoder.n_layers]
        # Initialize decoder input with SOS_token
        decoder_input = torch.ones(1, 1, device=device, dtype=torch.long) * SOS_token
        # Initialize tensors to append decoded words to
        all_tokens = torch.zeros([0], device=device, dtype=torch.long)
        all_scores = torch.zeros([0], device=device)
        # Iteratively decode one word token at a time
        for _ in range(max_length):
            # Forward pass through decoder
            decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs)
            # Obtain most likely word token and its softmax score
            decoder_scores, decoder_input = torch.max(decoder_output, dim=1)
            # Record token and score
            all_tokens = torch.cat((all_tokens, decoder_input), dim=0)
            all_scores = torch.cat((all_scores, decoder_scores), dim=0)
            # Prepare current token to be next decoder input (add a dimension)
            decoder_input = torch.unsqueeze(decoder_input, 0)
        # Return collections of word tokens and scores
        return all_tokens, all_scores

Evaluate my text

Now that we have our decoding method defined, we can write functions for evaluating a string input sentence. The evaluate function manages the low-level process of handling the input sentence. We first format the sentence as an input batch of word indexes with batch_size==1. We do this by converting the words of the sentence to their corresponding indexes, and transposing the dimensions to prepare the tensor for our models. We also create a lengths tensor which contains the length of our input sentence. In this case, lengths is scalar because we are only evaluating one sentence at a time (batch_size==1). Next, we obtain the decoded response sentence tensor using our GreedySearchDecoder object (searcher). Finally, we convert the response’s indexes to words and return the list of decoded words.

evaluateInput acts as the user interface for our chatbot. When called, an input text field will spawn in which we can enter our query sentence. After typing our input sentence and pressing Enter, our text is normalized in the same way as our training data, and is ultimately fed to the evaluate function to obtain a decoded output sentence. We loop this process, so we can keep chatting with our bot until we enter either “q” or “quit”.

Finally, if a sentence is entered that contains a word that is not in the vocabulary, we handle this gracefully by printing an error message and prompting the user to enter another sentence.

def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
    ### Format input sentence as a batch
    # words -> indexes
    indexes_batch = [indexesFromSentence(voc, sentence)]
    # Create lengths tensor
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    # Transpose dimensions of batch to match models' expectations
    input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
    # Use appropriate device
    input_batch = input_batch.to(device)
    lengths = lengths.to("cpu")
    # Decode sentence with searcher
    tokens, scores = searcher(input_batch, lengths, max_length)
    # indexes -> words
    decoded_words = [voc.index2word[token.item()] for token in tokens]
    return decoded_words


def evaluateInput(encoder, decoder, searcher, voc):
    input_sentence = ''
    while(1):
        try:
            # Get input sentence
            input_sentence = input('> ')
            # Check if it is quit case
            if input_sentence == 'q' or input_sentence == 'quit': break
            # Normalize sentence
            input_sentence = normalizeString(input_sentence)
            # Evaluate sentence
            output_words = evaluate(encoder, decoder, searcher, voc, input_sentence)
            # Format and print response sentence
            output_words[:] = [x for x in output_words if not (x == 'EOS' or x == 'PAD')]
            print('Bot:', ' '.join(output_words))

        except KeyError:
            print("Error: Encountered unknown word.")

Run Model

Finally, it is time to run our model!

Regardless of whether we want to train or test the chatbot model, we must initialize the individual encoder and decoder models. In the following block, we set our desired configurations, choose to start from scratch or set a checkpoint to load from, and build and initialize the models. Feel free to play with different model configurations to optimize performance.

# Configure models
model_name = 'cb_model'
attn_model = 'dot'
#attn_model = 'general'
#attn_model = 'concat'
hidden_size = 500
encoder_n_layers = 2
decoder_n_layers = 2
dropout = 0.1
batch_size = 64

# Set checkpoint to load from; set to None if starting from scratch
loadFilename = None
checkpoint_iter = 4000
#loadFilename = os.path.join(save_dir, model_name, corpus_name,
#                            '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
#                            '{}_checkpoint.tar'.format(checkpoint_iter))


# Load model if a loadFilename is provided
if loadFilename:
    # If loading on same machine the model was trained on
    checkpoint = torch.load(loadFilename)
    # If loading a model trained on GPU to CPU
    #checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))
    encoder_sd = checkpoint['en']
    decoder_sd = checkpoint['de']
    encoder_optimizer_sd = checkpoint['en_opt']
    decoder_optimizer_sd = checkpoint['de_opt']
    embedding_sd = checkpoint['embedding']
    voc.__dict__ = checkpoint['voc_dict']


print('Building encoder and decoder ...')
# Initialize word embeddings
embedding = nn.Embedding(voc.num_words, hidden_size)
if loadFilename:
    embedding.load_state_dict(embedding_sd)
# Initialize encoder & decoder models
encoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)
decoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size, voc.num_words, decoder_n_layers, dropout)
if loadFilename:
    encoder.load_state_dict(encoder_sd)
    decoder.load_state_dict(decoder_sd)
# Use appropriate device
encoder = encoder.to(device)
decoder = decoder.to(device)
print('Models built and ready to go!')

Out:

Building encoder and decoder ...
Models built and ready to go!

Run Training

Run the following block if you want to train the model.

First we set training parameters, then we initialize our optimizers, and finally we call the trainIters function to run our training iterations.

# Configure training/optimization
clip = 50.0
teacher_forcing_ratio = 1.0
learning_rate = 0.0001
decoder_learning_ratio = 5.0
n_iteration = 4000
print_every = 1
save_every = 500

# Ensure dropout layers are in train mode
encoder.train()
decoder.train()

# Initialize optimizers
print('Building optimizers ...')
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)
if loadFilename:
    encoder_optimizer.load_state_dict(encoder_optimizer_sd)
    decoder_optimizer.load_state_dict(decoder_optimizer_sd)

# If you have cuda, configure cuda to call
for state in encoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.cuda()

for state in decoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.cuda()

# Run training iterations
print("Starting Training!")
trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,
           embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size,
           print_every, save_every, clip, corpus_name, loadFilename)

Out:

Building optimizers ...
Starting Training!
Initializing ...
Training...
Iteration: 1; Percent complete: 0.0%; Average loss: 8.9629
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8388
Iteration: 3; Percent complete: 0.1%; Average loss: 8.6444
Iteration: 4; Percent complete: 0.1%; Average loss: 8.3189
Iteration: 5; Percent complete: 0.1%; Average loss: 8.0092
Iteration: 6; Percent complete: 0.1%; Average loss: 7.4935
Iteration: 7; Percent complete: 0.2%; Average loss: 7.0148
Iteration: 8; Percent complete: 0.2%; Average loss: 6.8640
Iteration: 9; Percent complete: 0.2%; Average loss: 6.6820
Iteration: 10; Percent complete: 0.2%; Average loss: 6.4646
Iteration: 11; Percent complete: 0.3%; Average loss: 5.9790
Iteration: 12; Percent complete: 0.3%; Average loss: 5.6545
Iteration: 13; Percent complete: 0.3%; Average loss: 5.6590
Iteration: 14; Percent complete: 0.4%; Average loss: 5.6765
Iteration: 15; Percent complete: 0.4%; Average loss: 5.4573
Iteration: 16; Percent complete: 0.4%; Average loss: 5.2786
Iteration: 17; Percent complete: 0.4%; Average loss: 4.8989
Iteration: 18; Percent complete: 0.4%; Average loss: 5.0713
Iteration: 19; Percent complete: 0.5%; Average loss: 4.9850
Iteration: 20; Percent complete: 0.5%; Average loss: 4.9109
Iteration: 21; Percent complete: 0.5%; Average loss: 5.0215
Iteration: 22; Percent complete: 0.5%; Average loss: 5.0876
Iteration: 23; Percent complete: 0.6%; Average loss: 5.1534
Iteration: 24; Percent complete: 0.6%; Average loss: 5.0290
Iteration: 25; Percent complete: 0.6%; Average loss: 4.8927
Iteration: 26; Percent complete: 0.7%; Average loss: 4.9131
Iteration: 27; Percent complete: 0.7%; Average loss: 4.6528
Iteration: 28; Percent complete: 0.7%; Average loss: 4.7487
Iteration: 29; Percent complete: 0.7%; Average loss: 4.8157
Iteration: 30; Percent complete: 0.8%; Average loss: 4.7966
Iteration: 31; Percent complete: 0.8%; Average loss: 4.5670
Iteration: 32; Percent complete: 0.8%; Average loss: 4.8404
Iteration: 33; Percent complete: 0.8%; Average loss: 4.6577
Iteration: 34; Percent complete: 0.9%; Average loss: 4.9100
Iteration: 35; Percent complete: 0.9%; Average loss: 4.6487
Iteration: 36; Percent complete: 0.9%; Average loss: 4.7571
Iteration: 37; Percent complete: 0.9%; Average loss: 4.7288
Iteration: 38; Percent complete: 0.9%; Average loss: 4.5091
Iteration: 39; Percent complete: 1.0%; Average loss: 4.9270
Iteration: 40; Percent complete: 1.0%; Average loss: 4.7383
Iteration: 41; Percent complete: 1.0%; Average loss: 4.7045
Iteration: 42; Percent complete: 1.1%; Average loss: 4.6741
Iteration: 43; Percent complete: 1.1%; Average loss: 4.6958
Iteration: 44; Percent complete: 1.1%; Average loss: 4.7067
Iteration: 45; Percent complete: 1.1%; Average loss: 4.4503
Iteration: 46; Percent complete: 1.1%; Average loss: 4.6595
Iteration: 47; Percent complete: 1.2%; Average loss: 4.6720
Iteration: 48; Percent complete: 1.2%; Average loss: 4.5401
Iteration: 49; Percent complete: 1.2%; Average loss: 4.9595
Iteration: 50; Percent complete: 1.2%; Average loss: 4.6769
Iteration: 51; Percent complete: 1.3%; Average loss: 4.5908
Iteration: 52; Percent complete: 1.3%; Average loss: 4.5593
Iteration: 53; Percent complete: 1.3%; Average loss: 4.7357
Iteration: 54; Percent complete: 1.4%; Average loss: 4.5775
Iteration: 55; Percent complete: 1.4%; Average loss: 4.6121
Iteration: 56; Percent complete: 1.4%; Average loss: 4.5184
Iteration: 57; Percent complete: 1.4%; Average loss: 4.3677
Iteration: 58; Percent complete: 1.5%; Average loss: 4.6102
Iteration: 59; Percent complete: 1.5%; Average loss: 4.4586
Iteration: 60; Percent complete: 1.5%; Average loss: 4.6835
Iteration: 61; Percent complete: 1.5%; Average loss: 4.5612
Iteration: 62; Percent complete: 1.6%; Average loss: 4.5607
Iteration: 63; Percent complete: 1.6%; Average loss: 4.5855
Iteration: 64; Percent complete: 1.6%; Average loss: 4.5108
Iteration: 65; Percent complete: 1.6%; Average loss: 4.4535
Iteration: 66; Percent complete: 1.7%; Average loss: 4.4623
Iteration: 67; Percent complete: 1.7%; Average loss: 4.5677
Iteration: 68; Percent complete: 1.7%; Average loss: 4.3049
Iteration: 69; Percent complete: 1.7%; Average loss: 4.8192
Iteration: 70; Percent complete: 1.8%; Average loss: 4.6466
Iteration: 71; Percent complete: 1.8%; Average loss: 4.3410
Iteration: 72; Percent complete: 1.8%; Average loss: 4.7564
Iteration: 73; Percent complete: 1.8%; Average loss: 4.5063
Iteration: 74; Percent complete: 1.8%; Average loss: 4.4119
Iteration: 75; Percent complete: 1.9%; Average loss: 4.6795
Iteration: 76; Percent complete: 1.9%; Average loss: 4.8061
Iteration: 77; Percent complete: 1.9%; Average loss: 4.4741
Iteration: 78; Percent complete: 1.9%; Average loss: 4.4836
Iteration: 79; Percent complete: 2.0%; Average loss: 4.6556
Iteration: 80; Percent complete: 2.0%; Average loss: 4.5196
Iteration: 81; Percent complete: 2.0%; Average loss: 4.5427
Iteration: 82; Percent complete: 2.1%; Average loss: 4.4441
Iteration: 83; Percent complete: 2.1%; Average loss: 4.6330
Iteration: 84; Percent complete: 2.1%; Average loss: 4.5817
Iteration: 85; Percent complete: 2.1%; Average loss: 4.1554
Iteration: 86; Percent complete: 2.1%; Average loss: 4.5787
Iteration: 87; Percent complete: 2.2%; Average loss: 4.4444
Iteration: 88; Percent complete: 2.2%; Average loss: 4.2186
Iteration: 89; Percent complete: 2.2%; Average loss: 4.6207
Iteration: 90; Percent complete: 2.2%; Average loss: 4.3926
Iteration: 91; Percent complete: 2.3%; Average loss: 4.2889
Iteration: 92; Percent complete: 2.3%; Average loss: 4.4030
Iteration: 93; Percent complete: 2.3%; Average loss: 4.7040
Iteration: 94; Percent complete: 2.4%; Average loss: 4.3294
Iteration: 95; Percent complete: 2.4%; Average loss: 4.6995
Iteration: 96; Percent complete: 2.4%; Average loss: 4.0621
Iteration: 97; Percent complete: 2.4%; Average loss: 4.4389
Iteration: 98; Percent complete: 2.5%; Average loss: 4.4100
Iteration: 99; Percent complete: 2.5%; Average loss: 4.4450
Iteration: 100; Percent complete: 2.5%; Average loss: 4.3709
Iteration: 101; Percent complete: 2.5%; Average loss: 4.4296
Iteration: 102; Percent complete: 2.5%; Average loss: 4.5012
Iteration: 103; Percent complete: 2.6%; Average loss: 4.4495
Iteration: 104; Percent complete: 2.6%; Average loss: 4.5406
Iteration: 105; Percent complete: 2.6%; Average loss: 4.3014
Iteration: 106; Percent complete: 2.6%; Average loss: 4.3978
Iteration: 107; Percent complete: 2.7%; Average loss: 4.3526
Iteration: 108; Percent complete: 2.7%; Average loss: 4.4255
Iteration: 109; Percent complete: 2.7%; Average loss: 4.1217
Iteration: 110; Percent complete: 2.8%; Average loss: 4.3395
Iteration: 111; Percent complete: 2.8%; Average loss: 4.4239
Iteration: 112; Percent complete: 2.8%; Average loss: 4.4682
Iteration: 113; Percent complete: 2.8%; Average loss: 4.4540
Iteration: 114; Percent complete: 2.9%; Average loss: 4.2974
Iteration: 115; Percent complete: 2.9%; Average loss: 4.5754
Iteration: 116; Percent complete: 2.9%; Average loss: 4.3651
Iteration: 117; Percent complete: 2.9%; Average loss: 4.2733
Iteration: 118; Percent complete: 2.9%; Average loss: 4.6500
Iteration: 119; Percent complete: 3.0%; Average loss: 4.2516
Iteration: 120; Percent complete: 3.0%; Average loss: 4.3539
Iteration: 121; Percent complete: 3.0%; Average loss: 4.3058
Iteration: 122; Percent complete: 3.0%; Average loss: 4.4496
Iteration: 123; Percent complete: 3.1%; Average loss: 4.2993
Iteration: 124; Percent complete: 3.1%; Average loss: 4.1091
Iteration: 125; Percent complete: 3.1%; Average loss: 4.2590
Iteration: 126; Percent complete: 3.1%; Average loss: 4.3649
Iteration: 127; Percent complete: 3.2%; Average loss: 4.3168
Iteration: 128; Percent complete: 3.2%; Average loss: 4.4485
Iteration: 129; Percent complete: 3.2%; Average loss: 4.4165
Iteration: 130; Percent complete: 3.2%; Average loss: 4.5174
Iteration: 131; Percent complete: 3.3%; Average loss: 4.1923
Iteration: 132; Percent complete: 3.3%; Average loss: 4.3628
Iteration: 133; Percent complete: 3.3%; Average loss: 4.1148
Iteration: 134; Percent complete: 3.4%; Average loss: 4.3779
Iteration: 135; Percent complete: 3.4%; Average loss: 4.4533
Iteration: 136; Percent complete: 3.4%; Average loss: 4.3102
Iteration: 137; Percent complete: 3.4%; Average loss: 4.1236
Iteration: 138; Percent complete: 3.5%; Average loss: 4.1836
Iteration: 139; Percent complete: 3.5%; Average loss: 4.3787
Iteration: 140; Percent complete: 3.5%; Average loss: 4.1474
Iteration: 141; Percent complete: 3.5%; Average loss: 4.2084
Iteration: 142; Percent complete: 3.5%; Average loss: 4.3519
Iteration: 143; Percent complete: 3.6%; Average loss: 3.9646
Iteration: 144; Percent complete: 3.6%; Average loss: 4.1385
Iteration: 145; Percent complete: 3.6%; Average loss: 4.3992
Iteration: 146; Percent complete: 3.6%; Average loss: 4.2430
Iteration: 147; Percent complete: 3.7%; Average loss: 4.3416
Iteration: 148; Percent complete: 3.7%; Average loss: 4.1893
Iteration: 149; Percent complete: 3.7%; Average loss: 4.3970
Iteration: 150; Percent complete: 3.8%; Average loss: 4.3838
Iteration: 151; Percent complete: 3.8%; Average loss: 4.3286
Iteration: 152; Percent complete: 3.8%; Average loss: 4.0490
Iteration: 153; Percent complete: 3.8%; Average loss: 4.3122
Iteration: 154; Percent complete: 3.9%; Average loss: 4.2107
Iteration: 155; Percent complete: 3.9%; Average loss: 4.0340
Iteration: 156; Percent complete: 3.9%; Average loss: 4.1592
Iteration: 157; Percent complete: 3.9%; Average loss: 4.2793
Iteration: 158; Percent complete: 4.0%; Average loss: 4.1214
Iteration: 159; Percent complete: 4.0%; Average loss: 4.1835
Iteration: 160; Percent complete: 4.0%; Average loss: 4.1966
Iteration: 161; Percent complete: 4.0%; Average loss: 4.1042
Iteration: 162; Percent complete: 4.0%; Average loss: 4.3132
Iteration: 163; Percent complete: 4.1%; Average loss: 4.3132
Iteration: 164; Percent complete: 4.1%; Average loss: 4.2408
Iteration: 165; Percent complete: 4.1%; Average loss: 4.1393
Iteration: 166; Percent complete: 4.2%; Average loss: 4.1348
Iteration: 167; Percent complete: 4.2%; Average loss: 4.2178
Iteration: 168; Percent complete: 4.2%; Average loss: 4.2593
Iteration: 169; Percent complete: 4.2%; Average loss: 3.9459
Iteration: 170; Percent complete: 4.2%; Average loss: 4.2110
Iteration: 171; Percent complete: 4.3%; Average loss: 4.4519
Iteration: 172; Percent complete: 4.3%; Average loss: 3.9915
Iteration: 173; Percent complete: 4.3%; Average loss: 4.4544
Iteration: 174; Percent complete: 4.3%; Average loss: 4.3465
Iteration: 175; Percent complete: 4.4%; Average loss: 4.3850
Iteration: 176; Percent complete: 4.4%; Average loss: 4.2136
Iteration: 177; Percent complete: 4.4%; Average loss: 4.3628
Iteration: 178; Percent complete: 4.5%; Average loss: 4.0222
Iteration: 179; Percent complete: 4.5%; Average loss: 4.2755
Iteration: 180; Percent complete: 4.5%; Average loss: 4.3084
Iteration: 181; Percent complete: 4.5%; Average loss: 4.2865
Iteration: 182; Percent complete: 4.5%; Average loss: 4.1001
Iteration: 183; Percent complete: 4.6%; Average loss: 4.4859
Iteration: 184; Percent complete: 4.6%; Average loss: 3.7932
Iteration: 185; Percent complete: 4.6%; Average loss: 4.1575
Iteration: 186; Percent complete: 4.7%; Average loss: 4.1343
Iteration: 187; Percent complete: 4.7%; Average loss: 4.3965
Iteration: 188; Percent complete: 4.7%; Average loss: 4.3259
Iteration: 189; Percent complete: 4.7%; Average loss: 4.1842
Iteration: 190; Percent complete: 4.8%; Average loss: 3.9954
Iteration: 191; Percent complete: 4.8%; Average loss: 3.9313
Iteration: 192; Percent complete: 4.8%; Average loss: 4.1274
Iteration: 193; Percent complete: 4.8%; Average loss: 4.2260
Iteration: 194; Percent complete: 4.9%; Average loss: 4.3254
Iteration: 195; Percent complete: 4.9%; Average loss: 4.2429
Iteration: 196; Percent complete: 4.9%; Average loss: 3.9252
Iteration: 197; Percent complete: 4.9%; Average loss: 4.2553
Iteration: 198; Percent complete: 5.0%; Average loss: 4.1982
Iteration: 199; Percent complete: 5.0%; Average loss: 4.1372
Iteration: 200; Percent complete: 5.0%; Average loss: 4.0440
Iteration: 201; Percent complete: 5.0%; Average loss: 4.3954
Iteration: 202; Percent complete: 5.1%; Average loss: 3.8685
Iteration: 203; Percent complete: 5.1%; Average loss: 4.0483
Iteration: 204; Percent complete: 5.1%; Average loss: 4.1219
Iteration: 205; Percent complete: 5.1%; Average loss: 4.1384
Iteration: 206; Percent complete: 5.1%; Average loss: 4.2466
Iteration: 207; Percent complete: 5.2%; Average loss: 3.9747
Iteration: 208; Percent complete: 5.2%; Average loss: 4.2028
Iteration: 209; Percent complete: 5.2%; Average loss: 3.9321
Iteration: 210; Percent complete: 5.2%; Average loss: 3.8859
Iteration: 211; Percent complete: 5.3%; Average loss: 4.2148
Iteration: 212; Percent complete: 5.3%; Average loss: 4.0220
Iteration: 213; Percent complete: 5.3%; Average loss: 4.0583
Iteration: 214; Percent complete: 5.3%; Average loss: 4.3813
Iteration: 215; Percent complete: 5.4%; Average loss: 3.7744
Iteration: 216; Percent complete: 5.4%; Average loss: 3.7934
Iteration: 217; Percent complete: 5.4%; Average loss: 3.9971
Iteration: 218; Percent complete: 5.5%; Average loss: 4.1384
Iteration: 219; Percent complete: 5.5%; Average loss: 4.2840
Iteration: 220; Percent complete: 5.5%; Average loss: 4.0047
Iteration: 221; Percent complete: 5.5%; Average loss: 3.8215
Iteration: 222; Percent complete: 5.5%; Average loss: 4.0263
Iteration: 223; Percent complete: 5.6%; Average loss: 4.1760
Iteration: 224; Percent complete: 5.6%; Average loss: 3.9699
Iteration: 225; Percent complete: 5.6%; Average loss: 3.9214
Iteration: 226; Percent complete: 5.7%; Average loss: 3.9343
Iteration: 227; Percent complete: 5.7%; Average loss: 4.0686
Iteration: 228; Percent complete: 5.7%; Average loss: 3.9658
Iteration: 229; Percent complete: 5.7%; Average loss: 4.1357
Iteration: 230; Percent complete: 5.8%; Average loss: 3.7954
Iteration: 231; Percent complete: 5.8%; Average loss: 3.9048
Iteration: 232; Percent complete: 5.8%; Average loss: 4.1185
Iteration: 233; Percent complete: 5.8%; Average loss: 3.8749
Iteration: 234; Percent complete: 5.9%; Average loss: 3.7139
Iteration: 235; Percent complete: 5.9%; Average loss: 4.1957
Iteration: 236; Percent complete: 5.9%; Average loss: 3.7558
Iteration: 237; Percent complete: 5.9%; Average loss: 4.0601
Iteration: 238; Percent complete: 5.9%; Average loss: 4.1140
Iteration: 239; Percent complete: 6.0%; Average loss: 4.0301
Iteration: 240; Percent complete: 6.0%; Average loss: 4.0303
Iteration: 241; Percent complete: 6.0%; Average loss: 3.9273
Iteration: 242; Percent complete: 6.0%; Average loss: 4.0104
Iteration: 243; Percent complete: 6.1%; Average loss: 4.0657
Iteration: 244; Percent complete: 6.1%; Average loss: 4.0914
Iteration: 245; Percent complete: 6.1%; Average loss: 3.8211
Iteration: 246; Percent complete: 6.2%; Average loss: 3.9776
Iteration: 247; Percent complete: 6.2%; Average loss: 3.9682
Iteration: 248; Percent complete: 6.2%; Average loss: 4.0536
Iteration: 249; Percent complete: 6.2%; Average loss: 4.0161
Iteration: 250; Percent complete: 6.2%; Average loss: 4.2089
Iteration: 251; Percent complete: 6.3%; Average loss: 4.3415
Iteration: 252; Percent complete: 6.3%; Average loss: 3.9016
Iteration: 253; Percent complete: 6.3%; Average loss: 4.1093
Iteration: 254; Percent complete: 6.3%; Average loss: 3.8623
Iteration: 255; Percent complete: 6.4%; Average loss: 4.2717
Iteration: 256; Percent complete: 6.4%; Average loss: 4.0283
Iteration: 257; Percent complete: 6.4%; Average loss: 3.9971
Iteration: 258; Percent complete: 6.5%; Average loss: 3.7143
Iteration: 259; Percent complete: 6.5%; Average loss: 3.8627
Iteration: 260; Percent complete: 6.5%; Average loss: 3.7108
Iteration: 261; Percent complete: 6.5%; Average loss: 3.9276
Iteration: 262; Percent complete: 6.6%; Average loss: 3.9930
Iteration: 263; Percent complete: 6.6%; Average loss: 4.2237
Iteration: 264; Percent complete: 6.6%; Average loss: 3.9874
Iteration: 265; Percent complete: 6.6%; Average loss: 3.9936
Iteration: 266; Percent complete: 6.7%; Average loss: 3.8960
Iteration: 267; Percent complete: 6.7%; Average loss: 3.8834
Iteration: 268; Percent complete: 6.7%; Average loss: 4.0111
Iteration: 269; Percent complete: 6.7%; Average loss: 3.7889
Iteration: 270; Percent complete: 6.8%; Average loss: 4.0093
Iteration: 271; Percent complete: 6.8%; Average loss: 3.9752
Iteration: 272; Percent complete: 6.8%; Average loss: 4.2249
Iteration: 273; Percent complete: 6.8%; Average loss: 3.8666
Iteration: 274; Percent complete: 6.9%; Average loss: 4.0485
Iteration: 275; Percent complete: 6.9%; Average loss: 3.9244
Iteration: 276; Percent complete: 6.9%; Average loss: 4.0170
Iteration: 277; Percent complete: 6.9%; Average loss: 3.8778
Iteration: 278; Percent complete: 7.0%; Average loss: 3.8872
Iteration: 279; Percent complete: 7.0%; Average loss: 3.9314
Iteration: 280; Percent complete: 7.0%; Average loss: 4.0433
Iteration: 281; Percent complete: 7.0%; Average loss: 3.5266
Iteration: 282; Percent complete: 7.0%; Average loss: 3.7145
Iteration: 283; Percent complete: 7.1%; Average loss: 3.8769
Iteration: 284; Percent complete: 7.1%; Average loss: 3.8622
Iteration: 285; Percent complete: 7.1%; Average loss: 4.0725
Iteration: 286; Percent complete: 7.1%; Average loss: 3.6240
Iteration: 287; Percent complete: 7.2%; Average loss: 3.8082
Iteration: 288; Percent complete: 7.2%; Average loss: 3.7193
Iteration: 289; Percent complete: 7.2%; Average loss: 3.8343
Iteration: 290; Percent complete: 7.2%; Average loss: 4.1128
Iteration: 291; Percent complete: 7.3%; Average loss: 3.6404
Iteration: 292; Percent complete: 7.3%; Average loss: 3.9180
Iteration: 293; Percent complete: 7.3%; Average loss: 4.0639
Iteration: 294; Percent complete: 7.3%; Average loss: 3.8461
Iteration: 295; Percent complete: 7.4%; Average loss: 4.0175
Iteration: 296; Percent complete: 7.4%; Average loss: 3.8406
Iteration: 297; Percent complete: 7.4%; Average loss: 3.9231
Iteration: 298; Percent complete: 7.4%; Average loss: 3.7808
Iteration: 299; Percent complete: 7.5%; Average loss: 3.8695
Iteration: 300; Percent complete: 7.5%; Average loss: 4.0292
Iteration: 301; Percent complete: 7.5%; Average loss: 3.6302
Iteration: 302; Percent complete: 7.5%; Average loss: 3.9456
Iteration: 303; Percent complete: 7.6%; Average loss: 3.6907
Iteration: 304; Percent complete: 7.6%; Average loss: 3.7721
Iteration: 305; Percent complete: 7.6%; Average loss: 3.8126
Iteration: 306; Percent complete: 7.6%; Average loss: 3.7905
Iteration: 307; Percent complete: 7.7%; Average loss: 3.7543
Iteration: 308; Percent complete: 7.7%; Average loss: 4.0811
Iteration: 309; Percent complete: 7.7%; Average loss: 3.8730
Iteration: 310; Percent complete: 7.8%; Average loss: 3.8109
Iteration: 311; Percent complete: 7.8%; Average loss: 3.9078
Iteration: 312; Percent complete: 7.8%; Average loss: 3.6995
Iteration: 313; Percent complete: 7.8%; Average loss: 3.8373
Iteration: 314; Percent complete: 7.8%; Average loss: 3.7665
Iteration: 315; Percent complete: 7.9%; Average loss: 4.0266
Iteration: 316; Percent complete: 7.9%; Average loss: 4.0595
Iteration: 317; Percent complete: 7.9%; Average loss: 3.8338
Iteration: 318; Percent complete: 8.0%; Average loss: 3.8761
Iteration: 319; Percent complete: 8.0%; Average loss: 3.7449
Iteration: 320; Percent complete: 8.0%; Average loss: 3.7520
Iteration: 321; Percent complete: 8.0%; Average loss: 3.8353
Iteration: 322; Percent complete: 8.1%; Average loss: 3.9623
Iteration: 323; Percent complete: 8.1%; Average loss: 3.8557
Iteration: 324; Percent complete: 8.1%; Average loss: 3.8033
Iteration: 325; Percent complete: 8.1%; Average loss: 3.9915
Iteration: 326; Percent complete: 8.2%; Average loss: 3.6721
Iteration: 327; Percent complete: 8.2%; Average loss: 3.9453
Iteration: 328; Percent complete: 8.2%; Average loss: 3.9105
Iteration: 329; Percent complete: 8.2%; Average loss: 3.9915
Iteration: 330; Percent complete: 8.2%; Average loss: 3.9963
Iteration: 331; Percent complete: 8.3%; Average loss: 4.0853
Iteration: 332; Percent complete: 8.3%; Average loss: 3.7873
Iteration: 333; Percent complete: 8.3%; Average loss: 3.5848
Iteration: 334; Percent complete: 8.3%; Average loss: 3.8860
Iteration: 335; Percent complete: 8.4%; Average loss: 3.8973
Iteration: 336; Percent complete: 8.4%; Average loss: 4.0261
Iteration: 337; Percent complete: 8.4%; Average loss: 3.7677
Iteration: 338; Percent complete: 8.5%; Average loss: 3.5745
Iteration: 339; Percent complete: 8.5%; Average loss: 3.9226
Iteration: 340; Percent complete: 8.5%; Average loss: 3.7093
Iteration: 341; Percent complete: 8.5%; Average loss: 4.0011
Iteration: 342; Percent complete: 8.6%; Average loss: 4.0160
Iteration: 343; Percent complete: 8.6%; Average loss: 3.6789
Iteration: 344; Percent complete: 8.6%; Average loss: 3.8848
Iteration: 345; Percent complete: 8.6%; Average loss: 3.7972
Iteration: 346; Percent complete: 8.6%; Average loss: 3.7909
Iteration: 347; Percent complete: 8.7%; Average loss: 4.1121
Iteration: 348; Percent complete: 8.7%; Average loss: 3.5763
Iteration: 349; Percent complete: 8.7%; Average loss: 4.2688
Iteration: 350; Percent complete: 8.8%; Average loss: 3.7522
Iteration: 351; Percent complete: 8.8%; Average loss: 4.0912
Iteration: 352; Percent complete: 8.8%; Average loss: 4.0485
Iteration: 353; Percent complete: 8.8%; Average loss: 4.1495
Iteration: 354; Percent complete: 8.8%; Average loss: 3.8902
Iteration: 355; Percent complete: 8.9%; Average loss: 4.0892
Iteration: 356; Percent complete: 8.9%; Average loss: 3.8399
Iteration: 357; Percent complete: 8.9%; Average loss: 3.8241
Iteration: 358; Percent complete: 8.9%; Average loss: 3.6037
Iteration: 359; Percent complete: 9.0%; Average loss: 3.6160
Iteration: 360; Percent complete: 9.0%; Average loss: 3.8296
Iteration: 361; Percent complete: 9.0%; Average loss: 3.8287
Iteration: 362; Percent complete: 9.0%; Average loss: 4.1136
Iteration: 363; Percent complete: 9.1%; Average loss: 3.8975
Iteration: 364; Percent complete: 9.1%; Average loss: 3.9347
Iteration: 365; Percent complete: 9.1%; Average loss: 4.0758
Iteration: 366; Percent complete: 9.2%; Average loss: 4.0139
Iteration: 367; Percent complete: 9.2%; Average loss: 3.9316
Iteration: 368; Percent complete: 9.2%; Average loss: 3.8146
Iteration: 369; Percent complete: 9.2%; Average loss: 3.5611
Iteration: 370; Percent complete: 9.2%; Average loss: 3.9259
Iteration: 371; Percent complete: 9.3%; Average loss: 3.8657
Iteration: 372; Percent complete: 9.3%; Average loss: 3.8845
Iteration: 373; Percent complete: 9.3%; Average loss: 3.7628
Iteration: 374; Percent complete: 9.3%; Average loss: 3.8457
Iteration: 375; Percent complete: 9.4%; Average loss: 3.7451
Iteration: 376; Percent complete: 9.4%; Average loss: 3.8281
Iteration: 377; Percent complete: 9.4%; Average loss: 3.7869
Iteration: 378; Percent complete: 9.4%; Average loss: 3.8411
Iteration: 379; Percent complete: 9.5%; Average loss: 3.8674
Iteration: 380; Percent complete: 9.5%; Average loss: 4.0000
Iteration: 381; Percent complete: 9.5%; Average loss: 3.6946
Iteration: 382; Percent complete: 9.6%; Average loss: 4.1641
Iteration: 383; Percent complete: 9.6%; Average loss: 3.8278
Iteration: 384; Percent complete: 9.6%; Average loss: 3.9785
Iteration: 385; Percent complete: 9.6%; Average loss: 3.8935
Iteration: 386; Percent complete: 9.7%; Average loss: 3.7466
Iteration: 387; Percent complete: 9.7%; Average loss: 3.8592
Iteration: 388; Percent complete: 9.7%; Average loss: 3.7490
Iteration: 389; Percent complete: 9.7%; Average loss: 3.7998
Iteration: 390; Percent complete: 9.8%; Average loss: 3.6006
Iteration: 391; Percent complete: 9.8%; Average loss: 3.7211
Iteration: 392; Percent complete: 9.8%; Average loss: 3.5326
Iteration: 393; Percent complete: 9.8%; Average loss: 3.8230
Iteration: 394; Percent complete: 9.8%; Average loss: 3.8871
Iteration: 395; Percent complete: 9.9%; Average loss: 3.8137
Iteration: 396; Percent complete: 9.9%; Average loss: 3.7083
Iteration: 397; Percent complete: 9.9%; Average loss: 3.9892
Iteration: 398; Percent complete: 10.0%; Average loss: 3.7039
Iteration: 399; Percent complete: 10.0%; Average loss: 3.8509
Iteration: 400; Percent complete: 10.0%; Average loss: 3.9858
Iteration: 401; Percent complete: 10.0%; Average loss: 3.7937
Iteration: 402; Percent complete: 10.1%; Average loss: 3.7965
Iteration: 403; Percent complete: 10.1%; Average loss: 3.7870
Iteration: 404; Percent complete: 10.1%; Average loss: 3.7032
Iteration: 405; Percent complete: 10.1%; Average loss: 4.1023
Iteration: 406; Percent complete: 10.2%; Average loss: 3.8695
Iteration: 407; Percent complete: 10.2%; Average loss: 3.6440
Iteration: 408; Percent complete: 10.2%; Average loss: 3.8815
Iteration: 409; Percent complete: 10.2%; Average loss: 3.9382
Iteration: 410; Percent complete: 10.2%; Average loss: 3.6864
Iteration: 411; Percent complete: 10.3%; Average loss: 3.7374
Iteration: 412; Percent complete: 10.3%; Average loss: 3.6812
Iteration: 413; Percent complete: 10.3%; Average loss: 3.5713
Iteration: 414; Percent complete: 10.3%; Average loss: 3.6776
Iteration: 415; Percent complete: 10.4%; Average loss: 3.6937
Iteration: 416; Percent complete: 10.4%; Average loss: 3.8557
Iteration: 417; Percent complete: 10.4%; Average loss: 3.9850
Iteration: 418; Percent complete: 10.4%; Average loss: 3.4753
Iteration: 419; Percent complete: 10.5%; Average loss: 3.8254
Iteration: 420; Percent complete: 10.5%; Average loss: 3.6980
Iteration: 421; Percent complete: 10.5%; Average loss: 3.4888
Iteration: 422; Percent complete: 10.5%; Average loss: 3.8690
Iteration: 423; Percent complete: 10.6%; Average loss: 3.5284
Iteration: 424; Percent complete: 10.6%; Average loss: 3.7145
Iteration: 425; Percent complete: 10.6%; Average loss: 3.6638
Iteration: 426; Percent complete: 10.7%; Average loss: 3.6832
Iteration: 427; Percent complete: 10.7%; Average loss: 3.5184
Iteration: 428; Percent complete: 10.7%; Average loss: 3.7350
Iteration: 429; Percent complete: 10.7%; Average loss: 3.6872
Iteration: 430; Percent complete: 10.8%; Average loss: 3.9925
Iteration: 431; Percent complete: 10.8%; Average loss: 3.7913
Iteration: 432; Percent complete: 10.8%; Average loss: 3.6372
Iteration: 433; Percent complete: 10.8%; Average loss: 3.7932
Iteration: 434; Percent complete: 10.8%; Average loss: 3.9221
Iteration: 435; Percent complete: 10.9%; Average loss: 3.7280
Iteration: 436; Percent complete: 10.9%; Average loss: 3.6314
Iteration: 437; Percent complete: 10.9%; Average loss: 4.0775
Iteration: 438; Percent complete: 10.9%; Average loss: 3.7262
Iteration: 439; Percent complete: 11.0%; Average loss: 3.6899
Iteration: 440; Percent complete: 11.0%; Average loss: 3.8038
Iteration: 441; Percent complete: 11.0%; Average loss: 3.8304
Iteration: 442; Percent complete: 11.1%; Average loss: 4.0337
Iteration: 443; Percent complete: 11.1%; Average loss: 3.8485
Iteration: 444; Percent complete: 11.1%; Average loss: 3.6984
Iteration: 445; Percent complete: 11.1%; Average loss: 3.7849
Iteration: 446; Percent complete: 11.2%; Average loss: 3.6412
Iteration: 447; Percent complete: 11.2%; Average loss: 3.9976
Iteration: 448; Percent complete: 11.2%; Average loss: 3.7747
Iteration: 449; Percent complete: 11.2%; Average loss: 3.7696
Iteration: 450; Percent complete: 11.2%; Average loss: 3.7262
Iteration: 451; Percent complete: 11.3%; Average loss: 3.8773
Iteration: 452; Percent complete: 11.3%; Average loss: 3.7717
Iteration: 453; Percent complete: 11.3%; Average loss: 3.6292
Iteration: 454; Percent complete: 11.3%; Average loss: 3.8646
Iteration: 455; Percent complete: 11.4%; Average loss: 3.6179
Iteration: 456; Percent complete: 11.4%; Average loss: 3.6347
Iteration: 457; Percent complete: 11.4%; Average loss: 3.8349
Iteration: 458; Percent complete: 11.5%; Average loss: 3.6323
Iteration: 459; Percent complete: 11.5%; Average loss: 3.7729
Iteration: 460; Percent complete: 11.5%; Average loss: 3.9761
Iteration: 461; Percent complete: 11.5%; Average loss: 3.5912
Iteration: 462; Percent complete: 11.6%; Average loss: 4.0933
Iteration: 463; Percent complete: 11.6%; Average loss: 3.8000
Iteration: 464; Percent complete: 11.6%; Average loss: 3.8059
Iteration: 465; Percent complete: 11.6%; Average loss: 3.4994
Iteration: 466; Percent complete: 11.7%; Average loss: 3.7394
Iteration: 467; Percent complete: 11.7%; Average loss: 3.8059
Iteration: 468; Percent complete: 11.7%; Average loss: 3.6789
Iteration: 469; Percent complete: 11.7%; Average loss: 3.5554
Iteration: 470; Percent complete: 11.8%; Average loss: 3.7686
Iteration: 471; Percent complete: 11.8%; Average loss: 3.6934
Iteration: 472; Percent complete: 11.8%; Average loss: 3.6717
Iteration: 473; Percent complete: 11.8%; Average loss: 3.6132
Iteration: 474; Percent complete: 11.8%; Average loss: 3.9972
Iteration: 475; Percent complete: 11.9%; Average loss: 3.7955
Iteration: 476; Percent complete: 11.9%; Average loss: 3.9258
Iteration: 477; Percent complete: 11.9%; Average loss: 3.3955
Iteration: 478; Percent complete: 11.9%; Average loss: 3.8711
Iteration: 479; Percent complete: 12.0%; Average loss: 3.7192
Iteration: 480; Percent complete: 12.0%; Average loss: 3.7994
Iteration: 481; Percent complete: 12.0%; Average loss: 3.5926
Iteration: 482; Percent complete: 12.0%; Average loss: 3.7664
Iteration: 483; Percent complete: 12.1%; Average loss: 3.7748
Iteration: 484; Percent complete: 12.1%; Average loss: 3.6967
Iteration: 485; Percent complete: 12.1%; Average loss: 4.1101
Iteration: 486; Percent complete: 12.2%; Average loss: 3.5646
Iteration: 487; Percent complete: 12.2%; Average loss: 3.7792
Iteration: 488; Percent complete: 12.2%; Average loss: 4.0112
Iteration: 489; Percent complete: 12.2%; Average loss: 3.8767
Iteration: 490; Percent complete: 12.2%; Average loss: 3.7824
Iteration: 491; Percent complete: 12.3%; Average loss: 3.9366
Iteration: 492; Percent complete: 12.3%; Average loss: 3.8151
Iteration: 493; Percent complete: 12.3%; Average loss: 4.0469
Iteration: 494; Percent complete: 12.3%; Average loss: 3.6357
Iteration: 495; Percent complete: 12.4%; Average loss: 3.8311
Iteration: 496; Percent complete: 12.4%; Average loss: 3.5020
Iteration: 497; Percent complete: 12.4%; Average loss: 3.8704
Iteration: 498; Percent complete: 12.4%; Average loss: 3.5803
Iteration: 499; Percent complete: 12.5%; Average loss: 3.5812
Iteration: 500; Percent complete: 12.5%; Average loss: 3.7657
Iteration: 501; Percent complete: 12.5%; Average loss: 3.6456
Iteration: 502; Percent complete: 12.6%; Average loss: 3.6808
Iteration: 503; Percent complete: 12.6%; Average loss: 3.8079
Iteration: 504; Percent complete: 12.6%; Average loss: 3.9316
Iteration: 505; Percent complete: 12.6%; Average loss: 4.0299
Iteration: 506; Percent complete: 12.7%; Average loss: 3.6460
Iteration: 507; Percent complete: 12.7%; Average loss: 3.6242
Iteration: 508; Percent complete: 12.7%; Average loss: 3.7954
Iteration: 509; Percent complete: 12.7%; Average loss: 3.7308
Iteration: 510; Percent complete: 12.8%; Average loss: 3.8056
Iteration: 511; Percent complete: 12.8%; Average loss: 3.8360
Iteration: 512; Percent complete: 12.8%; Average loss: 3.5595
Iteration: 513; Percent complete: 12.8%; Average loss: 4.1040
Iteration: 514; Percent complete: 12.8%; Average loss: 3.6223
Iteration: 515; Percent complete: 12.9%; Average loss: 3.8884
Iteration: 516; Percent complete: 12.9%; Average loss: 3.6264
Iteration: 517; Percent complete: 12.9%; Average loss: 3.8661
Iteration: 518; Percent complete: 13.0%; Average loss: 3.4638
Iteration: 519; Percent complete: 13.0%; Average loss: 3.7929
Iteration: 520; Percent complete: 13.0%; Average loss: 3.6640
Iteration: 521; Percent complete: 13.0%; Average loss: 3.5002
Iteration: 522; Percent complete: 13.1%; Average loss: 3.8716
Iteration: 523; Percent complete: 13.1%; Average loss: 3.7106
Iteration: 524; Percent complete: 13.1%; Average loss: 3.7650
Iteration: 525; Percent complete: 13.1%; Average loss: 3.9619
Iteration: 526; Percent complete: 13.2%; Average loss: 3.4824
Iteration: 527; Percent complete: 13.2%; Average loss: 3.7594
Iteration: 528; Percent complete: 13.2%; Average loss: 3.6339
Iteration: 529; Percent complete: 13.2%; Average loss: 3.4226
Iteration: 530; Percent complete: 13.2%; Average loss: 3.8701
Iteration: 531; Percent complete: 13.3%; Average loss: 3.7530
Iteration: 532; Percent complete: 13.3%; Average loss: 3.4885
Iteration: 533; Percent complete: 13.3%; Average loss: 3.8417
Iteration: 534; Percent complete: 13.4%; Average loss: 4.1154
Iteration: 535; Percent complete: 13.4%; Average loss: 3.7026
Iteration: 536; Percent complete: 13.4%; Average loss: 3.8090
Iteration: 537; Percent complete: 13.4%; Average loss: 3.6901
Iteration: 538; Percent complete: 13.5%; Average loss: 3.7765
Iteration: 539; Percent complete: 13.5%; Average loss: 3.9834
Iteration: 540; Percent complete: 13.5%; Average loss: 3.7178
Iteration: 541; Percent complete: 13.5%; Average loss: 3.9781
Iteration: 542; Percent complete: 13.6%; Average loss: 3.5722
Iteration: 543; Percent complete: 13.6%; Average loss: 3.5518
Iteration: 544; Percent complete: 13.6%; Average loss: 3.8106
Iteration: 545; Percent complete: 13.6%; Average loss: 3.6603
Iteration: 546; Percent complete: 13.7%; Average loss: 3.5598
Iteration: 547; Percent complete: 13.7%; Average loss: 3.5994
Iteration: 548; Percent complete: 13.7%; Average loss: 3.7878
Iteration: 549; Percent complete: 13.7%; Average loss: 3.4623
Iteration: 550; Percent complete: 13.8%; Average loss: 3.7996
Iteration: 551; Percent complete: 13.8%; Average loss: 3.5544
Iteration: 552; Percent complete: 13.8%; Average loss: 4.0475
Iteration: 553; Percent complete: 13.8%; Average loss: 3.6835
Iteration: 554; Percent complete: 13.9%; Average loss: 3.5064
Iteration: 555; Percent complete: 13.9%; Average loss: 3.4932
Iteration: 556; Percent complete: 13.9%; Average loss: 3.6046
Iteration: 557; Percent complete: 13.9%; Average loss: 3.6225
Iteration: 558; Percent complete: 14.0%; Average loss: 3.4974
Iteration: 559; Percent complete: 14.0%; Average loss: 3.6321
Iteration: 560; Percent complete: 14.0%; Average loss: 3.7353
Iteration: 561; Percent complete: 14.0%; Average loss: 3.5104
Iteration: 562; Percent complete: 14.1%; Average loss: 3.9275
Iteration: 563; Percent complete: 14.1%; Average loss: 3.6452
Iteration: 564; Percent complete: 14.1%; Average loss: 3.5935
Iteration: 565; Percent complete: 14.1%; Average loss: 3.6121
Iteration: 566; Percent complete: 14.1%; Average loss: 3.6288
Iteration: 567; Percent complete: 14.2%; Average loss: 3.8392
Iteration: 568; Percent complete: 14.2%; Average loss: 4.0121
Iteration: 569; Percent complete: 14.2%; Average loss: 3.6628
Iteration: 570; Percent complete: 14.2%; Average loss: 3.7463
Iteration: 571; Percent complete: 14.3%; Average loss: 3.5631
Iteration: 572; Percent complete: 14.3%; Average loss: 3.5395
Iteration: 573; Percent complete: 14.3%; Average loss: 3.7217
Iteration: 574; Percent complete: 14.3%; Average loss: 3.7506
Iteration: 575; Percent complete: 14.4%; Average loss: 3.5963
Iteration: 576; Percent complete: 14.4%; Average loss: 3.6869
Iteration: 577; Percent complete: 14.4%; Average loss: 3.7932
Iteration: 578; Percent complete: 14.4%; Average loss: 3.7986
Iteration: 579; Percent complete: 14.5%; Average loss: 3.7385
Iteration: 580; Percent complete: 14.5%; Average loss: 3.6843
Iteration: 581; Percent complete: 14.5%; Average loss: 3.6280
Iteration: 582; Percent complete: 14.5%; Average loss: 3.9501
Iteration: 583; Percent complete: 14.6%; Average loss: 3.5519
Iteration: 584; Percent complete: 14.6%; Average loss: 3.3741
Iteration: 585; Percent complete: 14.6%; Average loss: 3.9248
Iteration: 586; Percent complete: 14.6%; Average loss: 3.3991
Iteration: 587; Percent complete: 14.7%; Average loss: 3.8030
Iteration: 588; Percent complete: 14.7%; Average loss: 3.8951
Iteration: 589; Percent complete: 14.7%; Average loss: 3.9590
Iteration: 590; Percent complete: 14.8%; Average loss: 3.6095
Iteration: 591; Percent complete: 14.8%; Average loss: 3.5898
Iteration: 592; Percent complete: 14.8%; Average loss: 3.8549
Iteration: 593; Percent complete: 14.8%; Average loss: 3.6504
Iteration: 594; Percent complete: 14.8%; Average loss: 3.6174
Iteration: 595; Percent complete: 14.9%; Average loss: 3.6742
Iteration: 596; Percent complete: 14.9%; Average loss: 3.5708
Iteration: 597; Percent complete: 14.9%; Average loss: 3.5332
Iteration: 598; Percent complete: 14.9%; Average loss: 3.4934
Iteration: 599; Percent complete: 15.0%; Average loss: 3.7145
Iteration: 600; Percent complete: 15.0%; Average loss: 3.7610
Iteration: 601; Percent complete: 15.0%; Average loss: 3.5204
Iteration: 602; Percent complete: 15.0%; Average loss: 3.5225
Iteration: 603; Percent complete: 15.1%; Average loss: 3.5265
Iteration: 604; Percent complete: 15.1%; Average loss: 3.8453
Iteration: 605; Percent complete: 15.1%; Average loss: 3.5898
Iteration: 606; Percent complete: 15.2%; Average loss: 3.5841
Iteration: 607; Percent complete: 15.2%; Average loss: 3.5574
Iteration: 608; Percent complete: 15.2%; Average loss: 3.3647
Iteration: 609; Percent complete: 15.2%; Average loss: 3.5516
Iteration: 610; Percent complete: 15.2%; Average loss: 3.5271
Iteration: 611; Percent complete: 15.3%; Average loss: 3.4692
Iteration: 612; Percent complete: 15.3%; Average loss: 3.7380
Iteration: 613; Percent complete: 15.3%; Average loss: 3.4173
Iteration: 614; Percent complete: 15.3%; Average loss: 3.8286
Iteration: 615; Percent complete: 15.4%; Average loss: 3.5258
Iteration: 616; Percent complete: 15.4%; Average loss: 3.6995
Iteration: 617; Percent complete: 15.4%; Average loss: 3.7458
Iteration: 618; Percent complete: 15.4%; Average loss: 3.6491
Iteration: 619; Percent complete: 15.5%; Average loss: 3.5307
Iteration: 620; Percent complete: 15.5%; Average loss: 3.6552
Iteration: 621; Percent complete: 15.5%; Average loss: 3.8415
Iteration: 622; Percent complete: 15.6%; Average loss: 3.8224
Iteration: 623; Percent complete: 15.6%; Average loss: 3.6322
Iteration: 624; Percent complete: 15.6%; Average loss: 3.6366
Iteration: 625; Percent complete: 15.6%; Average loss: 3.8887
Iteration: 626; Percent complete: 15.7%; Average loss: 3.6616
Iteration: 627; Percent complete: 15.7%; Average loss: 3.7853
Iteration: 628; Percent complete: 15.7%; Average loss: 3.5242
Iteration: 629; Percent complete: 15.7%; Average loss: 3.4726
Iteration: 630; Percent complete: 15.8%; Average loss: 3.7742
Iteration: 631; Percent complete: 15.8%; Average loss: 3.6847
Iteration: 632; Percent complete: 15.8%; Average loss: 3.3926
Iteration: 633; Percent complete: 15.8%; Average loss: 3.8412
Iteration: 634; Percent complete: 15.8%; Average loss: 3.4853
Iteration: 635; Percent complete: 15.9%; Average loss: 3.6030
Iteration: 636; Percent complete: 15.9%; Average loss: 3.4985
Iteration: 637; Percent complete: 15.9%; Average loss: 3.5338
Iteration: 638; Percent complete: 16.0%; Average loss: 3.7952
Iteration: 639; Percent complete: 16.0%; Average loss: 3.3379
Iteration: 640; Percent complete: 16.0%; Average loss: 3.6184
Iteration: 641; Percent complete: 16.0%; Average loss: 3.6428
Iteration: 642; Percent complete: 16.1%; Average loss: 3.3704
Iteration: 643; Percent complete: 16.1%; Average loss: 3.4447
Iteration: 644; Percent complete: 16.1%; Average loss: 3.9176
Iteration: 645; Percent complete: 16.1%; Average loss: 4.0710
Iteration: 646; Percent complete: 16.2%; Average loss: 3.5468
Iteration: 647; Percent complete: 16.2%; Average loss: 3.4603
Iteration: 648; Percent complete: 16.2%; Average loss: 3.5852
Iteration: 649; Percent complete: 16.2%; Average loss: 3.4159
Iteration: 650; Percent complete: 16.2%; Average loss: 3.8579
Iteration: 651; Percent complete: 16.3%; Average loss: 3.7453
Iteration: 652; Percent complete: 16.3%; Average loss: 3.4753
Iteration: 653; Percent complete: 16.3%; Average loss: 3.5798
Iteration: 654; Percent complete: 16.4%; Average loss: 3.7172
Iteration: 655; Percent complete: 16.4%; Average loss: 3.6164
Iteration: 656; Percent complete: 16.4%; Average loss: 3.6579
Iteration: 657; Percent complete: 16.4%; Average loss: 3.6648
Iteration: 658; Percent complete: 16.4%; Average loss: 3.6952
Iteration: 659; Percent complete: 16.5%; Average loss: 3.7410
Iteration: 660; Percent complete: 16.5%; Average loss: 3.8734
Iteration: 661; Percent complete: 16.5%; Average loss: 3.4218
Iteration: 662; Percent complete: 16.6%; Average loss: 3.5349
Iteration: 663; Percent complete: 16.6%; Average loss: 3.6307
Iteration: 664; Percent complete: 16.6%; Average loss: 3.6726
Iteration: 665; Percent complete: 16.6%; Average loss: 3.6289
Iteration: 666; Percent complete: 16.7%; Average loss: 3.5451
Iteration: 667; Percent complete: 16.7%; Average loss: 3.6474
Iteration: 668; Percent complete: 16.7%; Average loss: 3.6790
Iteration: 669; Percent complete: 16.7%; Average loss: 3.7111
Iteration: 670; Percent complete: 16.8%; Average loss: 3.4809
Iteration: 671; Percent complete: 16.8%; Average loss: 3.6625
Iteration: 672; Percent complete: 16.8%; Average loss: 3.8362
Iteration: 673; Percent complete: 16.8%; Average loss: 3.4468
Iteration: 674; Percent complete: 16.9%; Average loss: 3.6415
Iteration: 675; Percent complete: 16.9%; Average loss: 3.7937
Iteration: 676; Percent complete: 16.9%; Average loss: 3.6502
Iteration: 677; Percent complete: 16.9%; Average loss: 3.5661
Iteration: 678; Percent complete: 17.0%; Average loss: 3.3534
Iteration: 679; Percent complete: 17.0%; Average loss: 3.5814
Iteration: 680; Percent complete: 17.0%; Average loss: 3.5793
Iteration: 681; Percent complete: 17.0%; Average loss: 3.6308
Iteration: 682; Percent complete: 17.1%; Average loss: 3.8298
Iteration: 683; Percent complete: 17.1%; Average loss: 3.4815
Iteration: 684; Percent complete: 17.1%; Average loss: 3.6262
Iteration: 685; Percent complete: 17.1%; Average loss: 3.5629
Iteration: 686; Percent complete: 17.2%; Average loss: 3.7043
Iteration: 687; Percent complete: 17.2%; Average loss: 3.7896
Iteration: 688; Percent complete: 17.2%; Average loss: 3.8460
Iteration: 689; Percent complete: 17.2%; Average loss: 3.5578
Iteration: 690; Percent complete: 17.2%; Average loss: 3.7568
Iteration: 691; Percent complete: 17.3%; Average loss: 3.3920
Iteration: 692; Percent complete: 17.3%; Average loss: 3.7899
Iteration: 693; Percent complete: 17.3%; Average loss: 3.4699
Iteration: 694; Percent complete: 17.3%; Average loss: 3.7608
Iteration: 695; Percent complete: 17.4%; Average loss: 3.4826
Iteration: 696; Percent complete: 17.4%; Average loss: 3.5374
Iteration: 697; Percent complete: 17.4%; Average loss: 3.5789
Iteration: 698; Percent complete: 17.4%; Average loss: 3.5136
Iteration: 699; Percent complete: 17.5%; Average loss: 3.9345
Iteration: 700; Percent complete: 17.5%; Average loss: 3.7567
Iteration: 701; Percent complete: 17.5%; Average loss: 3.6318
Iteration: 702; Percent complete: 17.5%; Average loss: 4.0158
Iteration: 703; Percent complete: 17.6%; Average loss: 3.4798
Iteration: 704; Percent complete: 17.6%; Average loss: 3.7422
Iteration: 705; Percent complete: 17.6%; Average loss: 3.5759
Iteration: 706; Percent complete: 17.6%; Average loss: 3.4703
Iteration: 707; Percent complete: 17.7%; Average loss: 3.8169
Iteration: 708; Percent complete: 17.7%; Average loss: 3.8279
Iteration: 709; Percent complete: 17.7%; Average loss: 3.4460
Iteration: 710; Percent complete: 17.8%; Average loss: 3.6465
Iteration: 711; Percent complete: 17.8%; Average loss: 3.7021
Iteration: 712; Percent complete: 17.8%; Average loss: 3.6238
Iteration: 713; Percent complete: 17.8%; Average loss: 3.7204
Iteration: 714; Percent complete: 17.8%; Average loss: 3.6587
Iteration: 715; Percent complete: 17.9%; Average loss: 3.6625
Iteration: 716; Percent complete: 17.9%; Average loss: 3.4259
Iteration: 717; Percent complete: 17.9%; Average loss: 3.6501
Iteration: 718; Percent complete: 17.9%; Average loss: 3.6166
Iteration: 719; Percent complete: 18.0%; Average loss: 3.4427
Iteration: 720; Percent complete: 18.0%; Average loss: 3.5428
Iteration: 721; Percent complete: 18.0%; Average loss: 3.6487
Iteration: 722; Percent complete: 18.1%; Average loss: 3.7231
Iteration: 723; Percent complete: 18.1%; Average loss: 3.1846
Iteration: 724; Percent complete: 18.1%; Average loss: 3.5901
Iteration: 725; Percent complete: 18.1%; Average loss: 3.6498
Iteration: 726; Percent complete: 18.1%; Average loss: 3.4700
Iteration: 727; Percent complete: 18.2%; Average loss: 3.6339
Iteration: 728; Percent complete: 18.2%; Average loss: 3.4474
Iteration: 729; Percent complete: 18.2%; Average loss: 3.5587
Iteration: 730; Percent complete: 18.2%; Average loss: 3.5082
Iteration: 731; Percent complete: 18.3%; Average loss: 3.5910
Iteration: 732; Percent complete: 18.3%; Average loss: 3.7138
Iteration: 733; Percent complete: 18.3%; Average loss: 3.4803
Iteration: 734; Percent complete: 18.4%; Average loss: 3.7100
Iteration: 735; Percent complete: 18.4%; Average loss: 3.5805
Iteration: 736; Percent complete: 18.4%; Average loss: 3.6198
Iteration: 737; Percent complete: 18.4%; Average loss: 3.5292
Iteration: 738; Percent complete: 18.4%; Average loss: 3.6295
Iteration: 739; Percent complete: 18.5%; Average loss: 3.5965
Iteration: 740; Percent complete: 18.5%; Average loss: 3.3824
Iteration: 741; Percent complete: 18.5%; Average loss: 3.4774
Iteration: 742; Percent complete: 18.6%; Average loss: 3.7566
Iteration: 743; Percent complete: 18.6%; Average loss: 3.7584
Iteration: 744; Percent complete: 18.6%; Average loss: 3.7920
Iteration: 745; Percent complete: 18.6%; Average loss: 3.6300
Iteration: 746; Percent complete: 18.6%; Average loss: 3.6561
Iteration: 747; Percent complete: 18.7%; Average loss: 3.8077
Iteration: 748; Percent complete: 18.7%; Average loss: 3.3862
Iteration: 749; Percent complete: 18.7%; Average loss: 3.5757
Iteration: 750; Percent complete: 18.8%; Average loss: 3.4340
Iteration: 751; Percent complete: 18.8%; Average loss: 3.6772
Iteration: 752; Percent complete: 18.8%; Average loss: 3.4918
Iteration: 753; Percent complete: 18.8%; Average loss: 3.5677
Iteration: 754; Percent complete: 18.9%; Average loss: 3.7105
Iteration: 755; Percent complete: 18.9%; Average loss: 3.4298
Iteration: 756; Percent complete: 18.9%; Average loss: 3.4224
Iteration: 757; Percent complete: 18.9%; Average loss: 3.5132
Iteration: 758; Percent complete: 18.9%; Average loss: 3.7143
Iteration: 759; Percent complete: 19.0%; Average loss: 3.7826
Iteration: 760; Percent complete: 19.0%; Average loss: 3.5034
Iteration: 761; Percent complete: 19.0%; Average loss: 3.4928
Iteration: 762; Percent complete: 19.1%; Average loss: 3.5758
Iteration: 763; Percent complete: 19.1%; Average loss: 3.4769
Iteration: 764; Percent complete: 19.1%; Average loss: 3.6902
Iteration: 765; Percent complete: 19.1%; Average loss: 3.4364
Iteration: 766; Percent complete: 19.1%; Average loss: 3.6994
Iteration: 767; Percent complete: 19.2%; Average loss: 3.3644
Iteration: 768; Percent complete: 19.2%; Average loss: 3.6437
Iteration: 769; Percent complete: 19.2%; Average loss: 3.8114
Iteration: 770; Percent complete: 19.2%; Average loss: 3.4276
Iteration: 771; Percent complete: 19.3%; Average loss: 3.6301
Iteration: 772; Percent complete: 19.3%; Average loss: 3.5459
Iteration: 773; Percent complete: 19.3%; Average loss: 3.7119
Iteration: 774; Percent complete: 19.4%; Average loss: 3.7159
Iteration: 775; Percent complete: 19.4%; Average loss: 3.4005
Iteration: 776; Percent complete: 19.4%; Average loss: 3.6636
Iteration: 777; Percent complete: 19.4%; Average loss: 3.7640
Iteration: 778; Percent complete: 19.4%; Average loss: 3.5681
Iteration: 779; Percent complete: 19.5%; Average loss: 3.5439
Iteration: 780; Percent complete: 19.5%; Average loss: 3.2429
Iteration: 781; Percent complete: 19.5%; Average loss: 3.5252
Iteration: 782; Percent complete: 19.6%; Average loss: 3.5084
Iteration: 783; Percent complete: 19.6%; Average loss: 3.4232
Iteration: 784; Percent complete: 19.6%; Average loss: 3.4310
Iteration: 785; Percent complete: 19.6%; Average loss: 3.5778
Iteration: 786; Percent complete: 19.7%; Average loss: 3.7550
Iteration: 787; Percent complete: 19.7%; Average loss: 3.7310
Iteration: 788; Percent complete: 19.7%; Average loss: 3.8029
Iteration: 789; Percent complete: 19.7%; Average loss: 3.6412
Iteration: 790; Percent complete: 19.8%; Average loss: 3.6823
Iteration: 791; Percent complete: 19.8%; Average loss: 3.4071
Iteration: 792; Percent complete: 19.8%; Average loss: 3.5226
Iteration: 793; Percent complete: 19.8%; Average loss: 3.6149
Iteration: 794; Percent complete: 19.9%; Average loss: 3.5934
Iteration: 795; Percent complete: 19.9%; Average loss: 3.6057
Iteration: 796; Percent complete: 19.9%; Average loss: 3.4079
Iteration: 797; Percent complete: 19.9%; Average loss: 3.3182
Iteration: 798; Percent complete: 20.0%; Average loss: 3.5627
Iteration: 799; Percent complete: 20.0%; Average loss: 3.4170
Iteration: 800; Percent complete: 20.0%; Average loss: 3.4663
Iteration: 801; Percent complete: 20.0%; Average loss: 3.2168
Iteration: 802; Percent complete: 20.1%; Average loss: 3.3527
Iteration: 803; Percent complete: 20.1%; Average loss: 3.3583
Iteration: 804; Percent complete: 20.1%; Average loss: 3.6552
Iteration: 805; Percent complete: 20.1%; Average loss: 3.3139
Iteration: 806; Percent complete: 20.2%; Average loss: 3.4902
Iteration: 807; Percent complete: 20.2%; Average loss: 3.4846
Iteration: 808; Percent complete: 20.2%; Average loss: 3.6351
Iteration: 809; Percent complete: 20.2%; Average loss: 3.3505
Iteration: 810; Percent complete: 20.2%; Average loss: 3.6128
Iteration: 811; Percent complete: 20.3%; Average loss: 3.5886
Iteration: 812; Percent complete: 20.3%; Average loss: 3.6654
Iteration: 813; Percent complete: 20.3%; Average loss: 3.3883
Iteration: 814; Percent complete: 20.3%; Average loss: 3.5931
Iteration: 815; Percent complete: 20.4%; Average loss: 3.4478
Iteration: 816; Percent complete: 20.4%; Average loss: 3.5007
Iteration: 817; Percent complete: 20.4%; Average loss: 3.6486
Iteration: 818; Percent complete: 20.4%; Average loss: 3.3267
Iteration: 819; Percent complete: 20.5%; Average loss: 3.3825
Iteration: 820; Percent complete: 20.5%; Average loss: 3.3266
Iteration: 821; Percent complete: 20.5%; Average loss: 3.4138
Iteration: 822; Percent complete: 20.5%; Average loss: 3.3887
Iteration: 823; Percent complete: 20.6%; Average loss: 3.0637
Iteration: 824; Percent complete: 20.6%; Average loss: 3.5275
Iteration: 825; Percent complete: 20.6%; Average loss: 3.4381
Iteration: 826; Percent complete: 20.6%; Average loss: 3.3514
Iteration: 827; Percent complete: 20.7%; Average loss: 3.7158
Iteration: 828; Percent complete: 20.7%; Average loss: 3.6474
Iteration: 829; Percent complete: 20.7%; Average loss: 3.4885
Iteration: 830; Percent complete: 20.8%; Average loss: 3.6285
Iteration: 831; Percent complete: 20.8%; Average loss: 3.5663
Iteration: 832; Percent complete: 20.8%; Average loss: 3.4298
Iteration: 833; Percent complete: 20.8%; Average loss: 3.5380
Iteration: 834; Percent complete: 20.8%; Average loss: 3.6021
Iteration: 835; Percent complete: 20.9%; Average loss: 3.5354
Iteration: 836; Percent complete: 20.9%; Average loss: 3.5345
Iteration: 837; Percent complete: 20.9%; Average loss: 3.6452
Iteration: 838; Percent complete: 20.9%; Average loss: 3.3733
Iteration: 839; Percent complete: 21.0%; Average loss: 3.5988
Iteration: 840; Percent complete: 21.0%; Average loss: 3.5273
Iteration: 841; Percent complete: 21.0%; Average loss: 3.6153
Iteration: 842; Percent complete: 21.1%; Average loss: 3.5222
Iteration: 843; Percent complete: 21.1%; Average loss: 3.5054
Iteration: 844; Percent complete: 21.1%; Average loss: 3.8939
Iteration: 845; Percent complete: 21.1%; Average loss: 3.6308
Iteration: 846; Percent complete: 21.1%; Average loss: 3.6480
Iteration: 847; Percent complete: 21.2%; Average loss: 3.4811
Iteration: 848; Percent complete: 21.2%; Average loss: 3.3433
Iteration: 849; Percent complete: 21.2%; Average loss: 3.5375
Iteration: 850; Percent complete: 21.2%; Average loss: 3.6076
Iteration: 851; Percent complete: 21.3%; Average loss: 3.4178
Iteration: 852; Percent complete: 21.3%; Average loss: 3.2749
Iteration: 853; Percent complete: 21.3%; Average loss: 3.5292
Iteration: 854; Percent complete: 21.3%; Average loss: 3.4729
Iteration: 855; Percent complete: 21.4%; Average loss: 3.5245
Iteration: 856; Percent complete: 21.4%; Average loss: 3.3836
Iteration: 857; Percent complete: 21.4%; Average loss: 3.5850
Iteration: 858; Percent complete: 21.4%; Average loss: 3.5154
Iteration: 859; Percent complete: 21.5%; Average loss: 3.4636
Iteration: 860; Percent complete: 21.5%; Average loss: 3.5092
Iteration: 861; Percent complete: 21.5%; Average loss: 3.3648
Iteration: 862; Percent complete: 21.6%; Average loss: 3.3548
Iteration: 863; Percent complete: 21.6%; Average loss: 3.4798
Iteration: 864; Percent complete: 21.6%; Average loss: 3.5792
Iteration: 865; Percent complete: 21.6%; Average loss: 3.2191
Iteration: 866; Percent complete: 21.6%; Average loss: 3.5649
Iteration: 867; Percent complete: 21.7%; Average loss: 3.7286
Iteration: 868; Percent complete: 21.7%; Average loss: 3.5038
Iteration: 869; Percent complete: 21.7%; Average loss: 3.8844
Iteration: 870; Percent complete: 21.8%; Average loss: 3.4697
Iteration: 871; Percent complete: 21.8%; Average loss: 3.5063
Iteration: 872; Percent complete: 21.8%; Average loss: 3.5226
Iteration: 873; Percent complete: 21.8%; Average loss: 3.6888
Iteration: 874; Percent complete: 21.9%; Average loss: 3.6535
Iteration: 875; Percent complete: 21.9%; Average loss: 3.4248
Iteration: 876; Percent complete: 21.9%; Average loss: 3.3779
Iteration: 877; Percent complete: 21.9%; Average loss: 3.3891
Iteration: 878; Percent complete: 21.9%; Average loss: 3.3939
Iteration: 879; Percent complete: 22.0%; Average loss: 3.6111
Iteration: 880; Percent complete: 22.0%; Average loss: 3.6764
Iteration: 881; Percent complete: 22.0%; Average loss: 3.4706
Iteration: 882; Percent complete: 22.1%; Average loss: 3.3635
Iteration: 883; Percent complete: 22.1%; Average loss: 3.6575
Iteration: 884; Percent complete: 22.1%; Average loss: 3.5418
Iteration: 885; Percent complete: 22.1%; Average loss: 3.6755
Iteration: 886; Percent complete: 22.1%; Average loss: 3.2937
Iteration: 887; Percent complete: 22.2%; Average loss: 3.4937
Iteration: 888; Percent complete: 22.2%; Average loss: 3.5469
Iteration: 889; Percent complete: 22.2%; Average loss: 3.5200
Iteration: 890; Percent complete: 22.2%; Average loss: 3.3986
Iteration: 891; Percent complete: 22.3%; Average loss: 3.4611
Iteration: 892; Percent complete: 22.3%; Average loss: 3.3701
Iteration: 893; Percent complete: 22.3%; Average loss: 3.6353
Iteration: 894; Percent complete: 22.4%; Average loss: 3.4849
Iteration: 895; Percent complete: 22.4%; Average loss: 3.3099
Iteration: 896; Percent complete: 22.4%; Average loss: 3.4241
Iteration: 897; Percent complete: 22.4%; Average loss: 3.7787
Iteration: 898; Percent complete: 22.4%; Average loss: 3.3659
Iteration: 899; Percent complete: 22.5%; Average loss: 3.6348
Iteration: 900; Percent complete: 22.5%; Average loss: 3.4457
Iteration: 901; Percent complete: 22.5%; Average loss: 3.7020
Iteration: 902; Percent complete: 22.6%; Average loss: 3.3886
Iteration: 903; Percent complete: 22.6%; Average loss: 3.5493
Iteration: 904; Percent complete: 22.6%; Average loss: 3.4051
Iteration: 905; Percent complete: 22.6%; Average loss: 3.3144
Iteration: 906; Percent complete: 22.7%; Average loss: 3.6239
Iteration: 907; Percent complete: 22.7%; Average loss: 3.4361
Iteration: 908; Percent complete: 22.7%; Average loss: 3.6054
Iteration: 909; Percent complete: 22.7%; Average loss: 3.4578
Iteration: 910; Percent complete: 22.8%; Average loss: 3.6862
Iteration: 911; Percent complete: 22.8%; Average loss: 3.5786
Iteration: 912; Percent complete: 22.8%; Average loss: 3.5291
Iteration: 913; Percent complete: 22.8%; Average loss: 3.2001
Iteration: 914; Percent complete: 22.9%; Average loss: 3.3695
Iteration: 915; Percent complete: 22.9%; Average loss: 3.4785
Iteration: 916; Percent complete: 22.9%; Average loss: 3.7552
Iteration: 917; Percent complete: 22.9%; Average loss: 3.5848
Iteration: 918; Percent complete: 22.9%; Average loss: 3.4784
Iteration: 919; Percent complete: 23.0%; Average loss: 3.3895
Iteration: 920; Percent complete: 23.0%; Average loss: 3.5273
Iteration: 921; Percent complete: 23.0%; Average loss: 3.4833
Iteration: 922; Percent complete: 23.1%; Average loss: 3.3605
Iteration: 923; Percent complete: 23.1%; Average loss: 3.5385
Iteration: 924; Percent complete: 23.1%; Average loss: 3.5952
Iteration: 925; Percent complete: 23.1%; Average loss: 3.5042
Iteration: 926; Percent complete: 23.2%; Average loss: 3.3934
Iteration: 927; Percent complete: 23.2%; Average loss: 3.6656
Iteration: 928; Percent complete: 23.2%; Average loss: 3.5905
Iteration: 929; Percent complete: 23.2%; Average loss: 3.3976
Iteration: 930; Percent complete: 23.2%; Average loss: 3.7356
Iteration: 931; Percent complete: 23.3%; Average loss: 3.2865
Iteration: 932; Percent complete: 23.3%; Average loss: 3.6622
Iteration: 933; Percent complete: 23.3%; Average loss: 3.6285
Iteration: 934; Percent complete: 23.4%; Average loss: 3.5728
Iteration: 935; Percent complete: 23.4%; Average loss: 3.3687
Iteration: 936; Percent complete: 23.4%; Average loss: 3.5727
Iteration: 937; Percent complete: 23.4%; Average loss: 3.6445
Iteration: 938; Percent complete: 23.4%; Average loss: 3.2409
Iteration: 939; Percent complete: 23.5%; Average loss: 3.6528
Iteration: 940; Percent complete: 23.5%; Average loss: 3.5687
Iteration: 941; Percent complete: 23.5%; Average loss: 3.4345
Iteration: 942; Percent complete: 23.5%; Average loss: 3.3874
Iteration: 943; Percent complete: 23.6%; Average loss: 3.3290
Iteration: 944; Percent complete: 23.6%; Average loss: 3.5213
Iteration: 945; Percent complete: 23.6%; Average loss: 3.6007
Iteration: 946; Percent complete: 23.6%; Average loss: 3.6211
Iteration: 947; Percent complete: 23.7%; Average loss: 3.5431
Iteration: 948; Percent complete: 23.7%; Average loss: 3.5396
Iteration: 949; Percent complete: 23.7%; Average loss: 3.3436
Iteration: 950; Percent complete: 23.8%; Average loss: 3.1953
Iteration: 951; Percent complete: 23.8%; Average loss: 3.5035
Iteration: 952; Percent complete: 23.8%; Average loss: 3.5158
Iteration: 953; Percent complete: 23.8%; Average loss: 3.6508
Iteration: 954; Percent complete: 23.8%; Average loss: 3.5286
Iteration: 955; Percent complete: 23.9%; Average loss: 3.5493
Iteration: 956; Percent complete: 23.9%; Average loss: 3.4746
Iteration: 957; Percent complete: 23.9%; Average loss: 3.2651
Iteration: 958; Percent complete: 23.9%; Average loss: 3.2419
Iteration: 959; Percent complete: 24.0%; Average loss: 3.3173
Iteration: 960; Percent complete: 24.0%; Average loss: 3.7070
Iteration: 961; Percent complete: 24.0%; Average loss: 3.3123
Iteration: 962; Percent complete: 24.1%; Average loss: 3.1790
Iteration: 963; Percent complete: 24.1%; Average loss: 3.7285
Iteration: 964; Percent complete: 24.1%; Average loss: 3.6975
Iteration: 965; Percent complete: 24.1%; Average loss: 3.4813
Iteration: 966; Percent complete: 24.1%; Average loss: 3.3296
Iteration: 967; Percent complete: 24.2%; Average loss: 3.7713
Iteration: 968; Percent complete: 24.2%; Average loss: 3.4299
Iteration: 969; Percent complete: 24.2%; Average loss: 3.4942
Iteration: 970; Percent complete: 24.2%; Average loss: 3.7266
Iteration: 971; Percent complete: 24.3%; Average loss: 3.3956
Iteration: 972; Percent complete: 24.3%; Average loss: 3.2667
Iteration: 973; Percent complete: 24.3%; Average loss: 3.4557
Iteration: 974; Percent complete: 24.3%; Average loss: 3.4061
Iteration: 975; Percent complete: 24.4%; Average loss: 3.4317
Iteration: 976; Percent complete: 24.4%; Average loss: 3.5370
Iteration: 977; Percent complete: 24.4%; Average loss: 3.7308
Iteration: 978; Percent complete: 24.4%; Average loss: 3.5113
Iteration: 979; Percent complete: 24.5%; Average loss: 3.4965
Iteration: 980; Percent complete: 24.5%; Average loss: 3.3445
Iteration: 981; Percent complete: 24.5%; Average loss: 3.5340
Iteration: 982; Percent complete: 24.6%; Average loss: 3.2948
Iteration: 983; Percent complete: 24.6%; Average loss: 3.4433
Iteration: 984; Percent complete: 24.6%; Average loss: 3.5634
Iteration: 985; Percent complete: 24.6%; Average loss: 3.5660
Iteration: 986; Percent complete: 24.6%; Average loss: 3.1846
Iteration: 987; Percent complete: 24.7%; Average loss: 3.5838
Iteration: 988; Percent complete: 24.7%; Average loss: 3.4607
Iteration: 989; Percent complete: 24.7%; Average loss: 3.5223
Iteration: 990; Percent complete: 24.8%; Average loss: 3.6235
Iteration: 991; Percent complete: 24.8%; Average loss: 3.3850
Iteration: 992; Percent complete: 24.8%; Average loss: 3.5876
Iteration: 993; Percent complete: 24.8%; Average loss: 3.5852
Iteration: 994; Percent complete: 24.9%; Average loss: 3.3626
Iteration: 995; Percent complete: 24.9%; Average loss: 3.2120
Iteration: 996; Percent complete: 24.9%; Average loss: 3.3927
Iteration: 997; Percent complete: 24.9%; Average loss: 3.3400
Iteration: 998; Percent complete: 24.9%; Average loss: 3.4095
Iteration: 999; Percent complete: 25.0%; Average loss: 3.6975
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.3238
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.3300
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.3134
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.4510
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.6541
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.3204
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.3986
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.3264
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.2374
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.1723
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.6206
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.5294
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.6274
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.4135
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.4595
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.4805
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.5448
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.5766
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.1963
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.4354
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.2667
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.4439
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.5169
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.6851
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.4599
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.6040
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.2249
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.3861
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.4858
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.0929
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.6708
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.7465
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.7047
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.6353
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.6157
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.5296
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.5048
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.6502
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.2017
Iteration: 1039; Percent complete: 26.0%; Average loss: 3.5651
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.4471
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.6018
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.4573
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.3168
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.6981
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.5294
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.6230
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.5792
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.4080
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.5225
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.2490
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.4303
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.6126
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.3810
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.3698
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.3200
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.3220
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.3582
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.3490
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.2228
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.4306
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.3045
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.5764
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.4363
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.4172
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.3850
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.4027
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.5342
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.6360
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.6209
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.3456
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.3730
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.2327
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.4641
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.5018
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.4066
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.6746
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.3739
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.4671
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.4994
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.3021
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.5629
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.2630
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.3696
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.4487
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.3461
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.2943
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.2656
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.6255
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.3280
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.3793
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.5730
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.4602
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.4406
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.4335
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.4431
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.5351
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.5215
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.2547
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.2679
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.2101
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.3588
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.7031
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.3337
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.3118
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.2454
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.4839
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.3162
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.4141
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.3822
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.4613
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.4444
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.6727
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.5226
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.5432
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.3001
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.5137
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.5082
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.5849
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.4261
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.4375
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.2476
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.5192
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.3307
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.2884
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.4116
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.4151
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.2867
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.3931
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.3025
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.3235
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.4893
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.3894
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.6392
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.4124
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.4187
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.3355
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.3940
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.3184
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.2891
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.2690
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.4179
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.5437
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.6240
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.2756
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.6306
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.4734
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.6625
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.4789
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.3764
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.5695
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.5887
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.2714
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.4364
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.4247
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.3692
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.2134
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.2814
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.0547
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.2627
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.2600
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.4079
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.3247
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.4161
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.2618
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.3332
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.3218
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.2702
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.5246
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.9010
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.4068
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.4208
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.1336
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.5867
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.4385
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.2083
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.2373
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.2691
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.4947
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.7464
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.5376
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.5701
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.3624
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.2383
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.4825
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.3774
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.4587
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.5795
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.2296
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.3442
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.3913
Iteration: 1191; Percent complete: 29.8%; Average loss: 3.2696
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.3718
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.2880
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.4881
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.5160
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.2583
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.4893
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.4429
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.2475
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.4445
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.5223
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.4554
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.6136
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.2852
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.3400
Iteration: 1206; Percent complete: 30.1%; Average loss: 3.2522
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.2162
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.2530
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.1496
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.5013
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.3792
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.4414
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.1955
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.2885
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.4426
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.4957
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.4158
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.5232
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.2445
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.3181
Iteration: 1221; Percent complete: 30.5%; Average loss: 3.4525
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.2778
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.0539
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.3189
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.3118
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.1381
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.3334
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.1200
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.3444
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.3497
Iteration: 1231; Percent complete: 30.8%; Average loss: 2.9804
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.2505
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.2395
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.3885
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.4455
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.0575
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.5711
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.3625
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.4757
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.3339
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.4179
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.3034
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.0708
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.3149
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.4793
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.4552
Iteration: 1247; Percent complete: 31.2%; Average loss: 3.3025
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.6017
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.5327
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.4648
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.2845
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.4364
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.5195
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.4018
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.4033
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.3038
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.2490
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.3928
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.5544
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.4470
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.1260
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.5946
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.6068
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.2769
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.1987
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.3233
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.5338
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.6085
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.3302
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.4769
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.3464
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.4964
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.7081
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.3181
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.5605
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.3518
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.5260
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.2239
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.3225
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.5102
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.3318
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.5451
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.2868
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.3925
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.3283
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.2597
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.2187
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.0523
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.3324
Iteration: 1290; Percent complete: 32.2%; Average loss: 3.5509
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.4204
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.5038
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.5623
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.0971
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.3010
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.2294
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.2280
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.4424
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.1394
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.6505
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.3002
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.3617
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.2146
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.5687
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.3233
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.4393
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.4157
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.5722
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.5409
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.4489
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.1718
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.5354
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.1641
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.2514
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.3506
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.4381
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.8097
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.2518
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.1557
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.7198
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.1827
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.5765
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.2398
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.5197
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.6438
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.4222
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.0285
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.0687
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.1240
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.3991
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.7456
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.2703
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.1024
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.5686
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.2849
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.6241
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.2811
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.2643
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.3725
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.2322
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.2861
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.5287
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.3543
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.3422
Iteration: 1345; Percent complete: 33.6%; Average loss: 3.4698
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.3088
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.4567
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.6187
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.3483
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.6700
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.1276
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.0972
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.3137
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.0711
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.3470
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.2415
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.2141
Iteration: 1358; Percent complete: 34.0%; Average loss: 3.4630
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.5181
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.4811
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.3492
Iteration: 1362; Percent complete: 34.1%; Average loss: 3.4133
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.3696
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.5370
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.6189
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.2337
Iteration: 1367; Percent complete: 34.2%; Average loss: 3.2204
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.2141
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.3289
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.2807
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.1533
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.3475
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.6547
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.3879
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.4328
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.3869
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.1074
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.2734
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.5631
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.3013
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.2857
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.3939
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.2983
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.3926
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.2435
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.2825
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.3324
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.6700
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.2952
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.2415
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.0876
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.4045
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.4294
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.3566
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.4837
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.3823
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.2578
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.1766
Iteration: 1399; Percent complete: 35.0%; Average loss: 3.2686
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.5177
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.5525
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.3040
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.3258
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.6077
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.1407
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.4767
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.4037
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.4397
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.4158
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.3124
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.4295
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.3302
Iteration: 1413; Percent complete: 35.3%; Average loss: 3.3121
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.2308
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.3976
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.4083
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.2075
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.2398
Iteration: 1419; Percent complete: 35.5%; Average loss: 3.3659
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.2151
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.4356
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.1923
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.4291
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.4411
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.6169
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.3703
Iteration: 1427; Percent complete: 35.7%; Average loss: 3.4405
Iteration: 1428; Percent complete: 35.7%; Average loss: 3.3129
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.3157
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.2429
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.2585
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.3994
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.4451
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.3673
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.3598
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.4602
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.4607
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.1946
Iteration: 1439; Percent complete: 36.0%; Average loss: 2.9900
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.1347
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.0886
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.0461
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.3707
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.3487
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.3163
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.3498
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.2260
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.3847
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.5333
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.2347
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.3115
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.3235
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.3796
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.3188
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.6377
Iteration: 1456; Percent complete: 36.4%; Average loss: 3.3365
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.4254
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.5320
Iteration: 1459; Percent complete: 36.5%; Average loss: 3.1893
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.4862
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.1856
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.2235
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.4106
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.2998
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.3332
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.0590
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.2168
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.1794
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.3991
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.4846
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.7282
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.1721
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.3988
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.2515
Iteration: 1475; Percent complete: 36.9%; Average loss: 3.3309
Iteration: 1476; Percent complete: 36.9%; Average loss: 3.3688
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.2663
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.2062
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.1873
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.2848
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.2098
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.2349
Iteration: 1483; Percent complete: 37.1%; Average loss: 3.3463
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.3758
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.2698
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.3260
Iteration: 1487; Percent complete: 37.2%; Average loss: 2.9719
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.4195
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.2429
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.2226
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.3261
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.5312
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.3755
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.2910
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.2858
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.1672
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.1001
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.1842
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.2152
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.3749
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.4489
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.1816
Iteration: 1503; Percent complete: 37.6%; Average loss: 3.4160
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.1777
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.1466
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.5064
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.1930
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.4185
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.2057
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.4894
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.0798
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.2766
Iteration: 1513; Percent complete: 37.8%; Average loss: 2.8352
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.2784
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.3093
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.2133
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.2319
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.6733
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.1530
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.4373
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.2742
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.4838
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.4920
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.4369
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.4092
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.3388
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.5309
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.3228
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.4337
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.2788
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.3300
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.2275
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.3824
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.5020
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.1778
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.2908
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.2158
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.4511
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.2198
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.5466
Iteration: 1541; Percent complete: 38.5%; Average loss: 3.1433
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.0977
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.3210
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.4531
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.2869
Iteration: 1546; Percent complete: 38.6%; Average loss: 2.8914
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.4107
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.5572
Iteration: 1549; Percent complete: 38.7%; Average loss: 3.5660
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.3282
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.2048
Iteration: 1552; Percent complete: 38.8%; Average loss: 3.1799
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.1635
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.3873
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.5009
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.3623
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.3817
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.1458
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.2840
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.4017
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.3925
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.4585
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.1883
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.2749
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.2573
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.1284
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.2416
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.1784
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.3227
Iteration: 1570; Percent complete: 39.2%; Average loss: 3.1555
Iteration: 1571; Percent complete: 39.3%; Average loss: 3.4449
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.0618
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.2424
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.3468
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.0631
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.3816
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.0062
Iteration: 1578; Percent complete: 39.5%; Average loss: 3.5243
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.2313
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.2837
Iteration: 1581; Percent complete: 39.5%; Average loss: 3.3725
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.3034
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.4826
Iteration: 1584; Percent complete: 39.6%; Average loss: 3.0758
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.4523
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.0927
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.3110
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.2813
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.1568
Iteration: 1590; Percent complete: 39.8%; Average loss: 3.0365
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.5599
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.3337
Iteration: 1593; Percent complete: 39.8%; Average loss: 3.5027
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.0776
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.2282
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.3940
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.4493
Iteration: 1598; Percent complete: 40.0%; Average loss: 2.9684
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.4934
Iteration: 1600; Percent complete: 40.0%; Average loss: 3.0208
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.1560
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.3664
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.1840
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.3433
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.1957
Iteration: 1606; Percent complete: 40.2%; Average loss: 2.9610
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.3384
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.0852
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.3895
Iteration: 1610; Percent complete: 40.2%; Average loss: 3.2865
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.2989
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.2179
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.1713
Iteration: 1614; Percent complete: 40.4%; Average loss: 2.9740
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.0742
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.2912
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.1796
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.2257
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.3525
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.2604
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.2419
Iteration: 1622; Percent complete: 40.6%; Average loss: 3.2816
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.2939
Iteration: 1624; Percent complete: 40.6%; Average loss: 3.1995
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.3734
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.0754
Iteration: 1627; Percent complete: 40.7%; Average loss: 3.3916
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.6073
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.1818
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.3195
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.4134
Iteration: 1632; Percent complete: 40.8%; Average loss: 3.4809
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.1679
Iteration: 1634; Percent complete: 40.8%; Average loss: 2.9929
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.3519
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.2143
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.6438
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.2669
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.3729
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.4193
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.1442
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.2726
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.2854
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.1703
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.0524
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.3035
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.3687
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.3431
Iteration: 1649; Percent complete: 41.2%; Average loss: 3.1182
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.5739
Iteration: 1651; Percent complete: 41.3%; Average loss: 3.1523
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.2997
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.1573
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.2265
Iteration: 1655; Percent complete: 41.4%; Average loss: 3.3786
Iteration: 1656; Percent complete: 41.4%; Average loss: 3.4711
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.2296
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.3220
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.2434
Iteration: 1660; Percent complete: 41.5%; Average loss: 3.2206
Iteration: 1661; Percent complete: 41.5%; Average loss: 3.4191
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.4709
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.2864
Iteration: 1664; Percent complete: 41.6%; Average loss: 3.2082
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.2192
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.2864
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.2653
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.3216
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.0860
Iteration: 1670; Percent complete: 41.8%; Average loss: 2.8507
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.3598
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.2070
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.0831
Iteration: 1674; Percent complete: 41.9%; Average loss: 3.1181
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.2262
Iteration: 1676; Percent complete: 41.9%; Average loss: 3.2949
Iteration: 1677; Percent complete: 41.9%; Average loss: 2.9986
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.3380
Iteration: 1679; Percent complete: 42.0%; Average loss: 3.6798
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.2585
Iteration: 1681; Percent complete: 42.0%; Average loss: 2.9002
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.2093
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.3606
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.0059
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.1138
Iteration: 1686; Percent complete: 42.1%; Average loss: 2.9793
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.2793
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.4669
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.1591
Iteration: 1690; Percent complete: 42.2%; Average loss: 3.2547
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.2551
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.1622
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.2628
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.1918
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.2378
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.2575
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.0788
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.1978
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.3042
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.3900
Iteration: 1701; Percent complete: 42.5%; Average loss: 3.3288
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.0240
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.1155
Iteration: 1704; Percent complete: 42.6%; Average loss: 3.4211
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.3633
Iteration: 1706; Percent complete: 42.6%; Average loss: 2.9843
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.4146
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.2728
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.1202
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.3935
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.2325
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.1874
Iteration: 1713; Percent complete: 42.8%; Average loss: 3.1697
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.6296
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.0362
Iteration: 1716; Percent complete: 42.9%; Average loss: 3.2130
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.1848
Iteration: 1718; Percent complete: 43.0%; Average loss: 3.3353
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.3990
Iteration: 1720; Percent complete: 43.0%; Average loss: 3.4069
Iteration: 1721; Percent complete: 43.0%; Average loss: 3.1015
Iteration: 1722; Percent complete: 43.0%; Average loss: 3.2839
Iteration: 1723; Percent complete: 43.1%; Average loss: 3.2557
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.1370
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.2592
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.0967
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.3813
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.1337
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.1819
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.1112
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.4135
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.0178
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.0470
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.3056
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.2132
Iteration: 1736; Percent complete: 43.4%; Average loss: 3.0037
Iteration: 1737; Percent complete: 43.4%; Average loss: 3.2439
Iteration: 1738; Percent complete: 43.5%; Average loss: 3.2630
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.2080
Iteration: 1740; Percent complete: 43.5%; Average loss: 3.4469
Iteration: 1741; Percent complete: 43.5%; Average loss: 3.4371
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.2741
Iteration: 1743; Percent complete: 43.6%; Average loss: 3.2128
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.2245
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.1337
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.3129
Iteration: 1747; Percent complete: 43.7%; Average loss: 3.1885
Iteration: 1748; Percent complete: 43.7%; Average loss: 3.4266
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.0642
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.3466
Iteration: 1751; Percent complete: 43.8%; Average loss: 3.3571
Iteration: 1752; Percent complete: 43.8%; Average loss: 2.9967
Iteration: 1753; Percent complete: 43.8%; Average loss: 3.2214
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.1881
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.0608
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.1823
Iteration: 1757; Percent complete: 43.9%; Average loss: 3.1230
Iteration: 1758; Percent complete: 44.0%; Average loss: 2.9732
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.3603
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.3320
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.0600
Iteration: 1762; Percent complete: 44.0%; Average loss: 3.2439
Iteration: 1763; Percent complete: 44.1%; Average loss: 3.0312
Iteration: 1764; Percent complete: 44.1%; Average loss: 2.9660
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.0774
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.1859
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.1280
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.0891
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.2215
Iteration: 1770; Percent complete: 44.2%; Average loss: 3.1436
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.2214
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.0729
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.3452
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.2191
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.0619
Iteration: 1776; Percent complete: 44.4%; Average loss: 3.0042
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.3912
Iteration: 1778; Percent complete: 44.5%; Average loss: 3.1523
Iteration: 1779; Percent complete: 44.5%; Average loss: 3.1153
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.0581
Iteration: 1781; Percent complete: 44.5%; Average loss: 3.1022
Iteration: 1782; Percent complete: 44.5%; Average loss: 3.3362
Iteration: 1783; Percent complete: 44.6%; Average loss: 3.3370
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.1541
Iteration: 1785; Percent complete: 44.6%; Average loss: 3.2720
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.2124
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.0247
Iteration: 1788; Percent complete: 44.7%; Average loss: 3.0129
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.1401
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.3526
Iteration: 1791; Percent complete: 44.8%; Average loss: 2.9058
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.3266
Iteration: 1793; Percent complete: 44.8%; Average loss: 3.2302
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.1980
Iteration: 1795; Percent complete: 44.9%; Average loss: 3.0793
Iteration: 1796; Percent complete: 44.9%; Average loss: 3.2273
Iteration: 1797; Percent complete: 44.9%; Average loss: 3.1582
Iteration: 1798; Percent complete: 45.0%; Average loss: 3.1956
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.1619
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.0529
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.2666
Iteration: 1802; Percent complete: 45.1%; Average loss: 3.2195
Iteration: 1803; Percent complete: 45.1%; Average loss: 3.1603
Iteration: 1804; Percent complete: 45.1%; Average loss: 3.3302
Iteration: 1805; Percent complete: 45.1%; Average loss: 3.2324
Iteration: 1806; Percent complete: 45.1%; Average loss: 2.9247
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.2055
Iteration: 1808; Percent complete: 45.2%; Average loss: 3.1985
Iteration: 1809; Percent complete: 45.2%; Average loss: 2.9949
Iteration: 1810; Percent complete: 45.2%; Average loss: 3.1303
Iteration: 1811; Percent complete: 45.3%; Average loss: 3.3097
Iteration: 1812; Percent complete: 45.3%; Average loss: 3.2251
Iteration: 1813; Percent complete: 45.3%; Average loss: 3.1184
Iteration: 1814; Percent complete: 45.4%; Average loss: 3.2198
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.1815
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.2384
Iteration: 1817; Percent complete: 45.4%; Average loss: 2.9879
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.2264
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.3931
Iteration: 1820; Percent complete: 45.5%; Average loss: 3.4171
Iteration: 1821; Percent complete: 45.5%; Average loss: 2.9901
Iteration: 1822; Percent complete: 45.6%; Average loss: 3.6705
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.4533
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.5008
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.2050
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.1047
Iteration: 1827; Percent complete: 45.7%; Average loss: 3.3918
Iteration: 1828; Percent complete: 45.7%; Average loss: 3.0579
Iteration: 1829; Percent complete: 45.7%; Average loss: 2.9410
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.0225
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.1544
Iteration: 1832; Percent complete: 45.8%; Average loss: 3.1861
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.2981
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.3200
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.3633
Iteration: 1836; Percent complete: 45.9%; Average loss: 3.2055
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.1611
Iteration: 1838; Percent complete: 46.0%; Average loss: 3.3072
Iteration: 1839; Percent complete: 46.0%; Average loss: 3.3497
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.2828
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.0443
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.1435
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.1565
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.2554
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.0974
Iteration: 1846; Percent complete: 46.2%; Average loss: 3.3422
Iteration: 1847; Percent complete: 46.2%; Average loss: 3.2039
Iteration: 1848; Percent complete: 46.2%; Average loss: 2.9362
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.2320
Iteration: 1850; Percent complete: 46.2%; Average loss: 3.1869
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.1445
Iteration: 1852; Percent complete: 46.3%; Average loss: 3.2092
Iteration: 1853; Percent complete: 46.3%; Average loss: 3.4809
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.2598
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.1941
Iteration: 1856; Percent complete: 46.4%; Average loss: 2.9170
Iteration: 1857; Percent complete: 46.4%; Average loss: 3.2152
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.2781
Iteration: 1859; Percent complete: 46.5%; Average loss: 2.7819
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.2430
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.1331
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.1708
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.3222
Iteration: 1864; Percent complete: 46.6%; Average loss: 3.4938
Iteration: 1865; Percent complete: 46.6%; Average loss: 3.0034
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.2217
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.1976
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.3229
Iteration: 1869; Percent complete: 46.7%; Average loss: 3.3855
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.1395
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.2809
Iteration: 1872; Percent complete: 46.8%; Average loss: 2.8805
Iteration: 1873; Percent complete: 46.8%; Average loss: 3.4280
Iteration: 1874; Percent complete: 46.9%; Average loss: 3.4842
Iteration: 1875; Percent complete: 46.9%; Average loss: 3.2785
Iteration: 1876; Percent complete: 46.9%; Average loss: 2.9543
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.3172
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.1377
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.4027
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.1221
Iteration: 1881; Percent complete: 47.0%; Average loss: 3.3157
Iteration: 1882; Percent complete: 47.0%; Average loss: 3.1636
Iteration: 1883; Percent complete: 47.1%; Average loss: 3.2292
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.1071
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.0379
Iteration: 1886; Percent complete: 47.1%; Average loss: 2.8948
Iteration: 1887; Percent complete: 47.2%; Average loss: 3.1931
Iteration: 1888; Percent complete: 47.2%; Average loss: 3.3954
Iteration: 1889; Percent complete: 47.2%; Average loss: 3.1460
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.2415
Iteration: 1891; Percent complete: 47.3%; Average loss: 3.1567
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.1086
Iteration: 1893; Percent complete: 47.3%; Average loss: 3.0755
Iteration: 1894; Percent complete: 47.3%; Average loss: 2.9827
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.4287
Iteration: 1896; Percent complete: 47.4%; Average loss: 3.0579
Iteration: 1897; Percent complete: 47.4%; Average loss: 3.2566
Iteration: 1898; Percent complete: 47.4%; Average loss: 3.2380
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.0869
Iteration: 1900; Percent complete: 47.5%; Average loss: 2.9651
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.1136
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.4235
Iteration: 1903; Percent complete: 47.6%; Average loss: 3.2093
Iteration: 1904; Percent complete: 47.6%; Average loss: 3.1960
Iteration: 1905; Percent complete: 47.6%; Average loss: 3.1689
Iteration: 1906; Percent complete: 47.6%; Average loss: 3.2083
Iteration: 1907; Percent complete: 47.7%; Average loss: 3.0615
Iteration: 1908; Percent complete: 47.7%; Average loss: 3.2038
Iteration: 1909; Percent complete: 47.7%; Average loss: 3.3469
Iteration: 1910; Percent complete: 47.8%; Average loss: 2.9770
Iteration: 1911; Percent complete: 47.8%; Average loss: 3.3338
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.1556
Iteration: 1913; Percent complete: 47.8%; Average loss: 3.1848
Iteration: 1914; Percent complete: 47.9%; Average loss: 3.1052
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.3571
Iteration: 1916; Percent complete: 47.9%; Average loss: 3.1163
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.1703
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.1187
Iteration: 1919; Percent complete: 48.0%; Average loss: 3.0463
Iteration: 1920; Percent complete: 48.0%; Average loss: 3.2212
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.3998
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.2839
Iteration: 1923; Percent complete: 48.1%; Average loss: 3.1731
Iteration: 1924; Percent complete: 48.1%; Average loss: 3.1948
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.3924
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.1333
Iteration: 1927; Percent complete: 48.2%; Average loss: 3.2996
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.0909
Iteration: 1929; Percent complete: 48.2%; Average loss: 3.2970
Iteration: 1930; Percent complete: 48.2%; Average loss: 3.3079
Iteration: 1931; Percent complete: 48.3%; Average loss: 2.9620
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.4122
Iteration: 1933; Percent complete: 48.3%; Average loss: 3.3039
Iteration: 1934; Percent complete: 48.4%; Average loss: 3.3120
Iteration: 1935; Percent complete: 48.4%; Average loss: 3.0934
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.2156
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.3183
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.1565
Iteration: 1939; Percent complete: 48.5%; Average loss: 2.8508
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.3159
Iteration: 1941; Percent complete: 48.5%; Average loss: 3.3184
Iteration: 1942; Percent complete: 48.5%; Average loss: 3.4187
Iteration: 1943; Percent complete: 48.6%; Average loss: 3.2852
Iteration: 1944; Percent complete: 48.6%; Average loss: 3.3364
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.3974
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.2422
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.5353
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.2707
Iteration: 1949; Percent complete: 48.7%; Average loss: 3.0435
Iteration: 1950; Percent complete: 48.8%; Average loss: 3.0678
Iteration: 1951; Percent complete: 48.8%; Average loss: 3.1840
Iteration: 1952; Percent complete: 48.8%; Average loss: 3.1664
Iteration: 1953; Percent complete: 48.8%; Average loss: 3.1166
Iteration: 1954; Percent complete: 48.9%; Average loss: 3.0390
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.1380
Iteration: 1956; Percent complete: 48.9%; Average loss: 3.2062
Iteration: 1957; Percent complete: 48.9%; Average loss: 3.0471
Iteration: 1958; Percent complete: 48.9%; Average loss: 3.1714
Iteration: 1959; Percent complete: 49.0%; Average loss: 3.5495
Iteration: 1960; Percent complete: 49.0%; Average loss: 3.0273
Iteration: 1961; Percent complete: 49.0%; Average loss: 3.3701
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.2903
Iteration: 1963; Percent complete: 49.1%; Average loss: 3.2997
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.1396
Iteration: 1965; Percent complete: 49.1%; Average loss: 3.2028
Iteration: 1966; Percent complete: 49.1%; Average loss: 3.2020
Iteration: 1967; Percent complete: 49.2%; Average loss: 2.9713
Iteration: 1968; Percent complete: 49.2%; Average loss: 2.9278
Iteration: 1969; Percent complete: 49.2%; Average loss: 3.1354
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.2442
Iteration: 1971; Percent complete: 49.3%; Average loss: 3.3872
Iteration: 1972; Percent complete: 49.3%; Average loss: 3.1611
Iteration: 1973; Percent complete: 49.3%; Average loss: 3.3023
Iteration: 1974; Percent complete: 49.4%; Average loss: 3.0593
Iteration: 1975; Percent complete: 49.4%; Average loss: 3.0852
Iteration: 1976; Percent complete: 49.4%; Average loss: 3.2615
Iteration: 1977; Percent complete: 49.4%; Average loss: 3.0925
Iteration: 1978; Percent complete: 49.5%; Average loss: 3.1706
Iteration: 1979; Percent complete: 49.5%; Average loss: 3.3822
Iteration: 1980; Percent complete: 49.5%; Average loss: 3.2726
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.3341
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.1234
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.1279
Iteration: 1984; Percent complete: 49.6%; Average loss: 3.0570
Iteration: 1985; Percent complete: 49.6%; Average loss: 3.0668
Iteration: 1986; Percent complete: 49.6%; Average loss: 3.2060
Iteration: 1987; Percent complete: 49.7%; Average loss: 2.7927
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.0660
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.1455
Iteration: 1990; Percent complete: 49.8%; Average loss: 3.3105
Iteration: 1991; Percent complete: 49.8%; Average loss: 3.1872
Iteration: 1992; Percent complete: 49.8%; Average loss: 2.9123
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.0129
Iteration: 1994; Percent complete: 49.9%; Average loss: 2.9628
Iteration: 1995; Percent complete: 49.9%; Average loss: 2.9280
Iteration: 1996; Percent complete: 49.9%; Average loss: 3.1683
Iteration: 1997; Percent complete: 49.9%; Average loss: 3.1227
Iteration: 1998; Percent complete: 50.0%; Average loss: 3.1564
Iteration: 1999; Percent complete: 50.0%; Average loss: 3.2002
Iteration: 2000; Percent complete: 50.0%; Average loss: 3.0671
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.1484
Iteration: 2002; Percent complete: 50.0%; Average loss: 3.1645
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.4161
Iteration: 2004; Percent complete: 50.1%; Average loss: 3.1333
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.2690
Iteration: 2006; Percent complete: 50.1%; Average loss: 3.4805
Iteration: 2007; Percent complete: 50.2%; Average loss: 3.0690
Iteration: 2008; Percent complete: 50.2%; Average loss: 3.3156
Iteration: 2009; Percent complete: 50.2%; Average loss: 3.0460
Iteration: 2010; Percent complete: 50.2%; Average loss: 3.1474
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.2467
Iteration: 2012; Percent complete: 50.3%; Average loss: 2.9935
Iteration: 2013; Percent complete: 50.3%; Average loss: 3.0330
Iteration: 2014; Percent complete: 50.3%; Average loss: 3.1673
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.0940
Iteration: 2016; Percent complete: 50.4%; Average loss: 3.0681
Iteration: 2017; Percent complete: 50.4%; Average loss: 3.0936
Iteration: 2018; Percent complete: 50.4%; Average loss: 2.9493
Iteration: 2019; Percent complete: 50.5%; Average loss: 2.8708
Iteration: 2020; Percent complete: 50.5%; Average loss: 2.9645
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.2069
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.0871
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.1677
Iteration: 2024; Percent complete: 50.6%; Average loss: 3.0896
Iteration: 2025; Percent complete: 50.6%; Average loss: 3.1584
Iteration: 2026; Percent complete: 50.6%; Average loss: 3.1960
Iteration: 2027; Percent complete: 50.7%; Average loss: 3.3195
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.2524
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.2200
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.1459
Iteration: 2031; Percent complete: 50.8%; Average loss: 2.9585
Iteration: 2032; Percent complete: 50.8%; Average loss: 3.2658
Iteration: 2033; Percent complete: 50.8%; Average loss: 3.3357
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.2043
Iteration: 2035; Percent complete: 50.9%; Average loss: 2.8979
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.2469
Iteration: 2037; Percent complete: 50.9%; Average loss: 3.5333
Iteration: 2038; Percent complete: 50.9%; Average loss: 3.1721
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.3164
Iteration: 2040; Percent complete: 51.0%; Average loss: 3.0058
Iteration: 2041; Percent complete: 51.0%; Average loss: 3.0456
Iteration: 2042; Percent complete: 51.0%; Average loss: 2.9346
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.2504
Iteration: 2044; Percent complete: 51.1%; Average loss: 2.9935
Iteration: 2045; Percent complete: 51.1%; Average loss: 3.2115
Iteration: 2046; Percent complete: 51.1%; Average loss: 3.0234
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.0950
Iteration: 2048; Percent complete: 51.2%; Average loss: 2.9763
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.0151
Iteration: 2050; Percent complete: 51.2%; Average loss: 3.4321
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.2886
Iteration: 2052; Percent complete: 51.3%; Average loss: 3.1198
Iteration: 2053; Percent complete: 51.3%; Average loss: 3.1047
Iteration: 2054; Percent complete: 51.3%; Average loss: 2.9434
Iteration: 2055; Percent complete: 51.4%; Average loss: 3.3286
Iteration: 2056; Percent complete: 51.4%; Average loss: 3.2913
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.3969
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.0731
Iteration: 2059; Percent complete: 51.5%; Average loss: 3.2538
Iteration: 2060; Percent complete: 51.5%; Average loss: 3.3125
Iteration: 2061; Percent complete: 51.5%; Average loss: 3.0047
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.0680
Iteration: 2063; Percent complete: 51.6%; Average loss: 3.1179
Iteration: 2064; Percent complete: 51.6%; Average loss: 3.2065
Iteration: 2065; Percent complete: 51.6%; Average loss: 3.1535
Iteration: 2066; Percent complete: 51.6%; Average loss: 3.1816
Iteration: 2067; Percent complete: 51.7%; Average loss: 2.9877
Iteration: 2068; Percent complete: 51.7%; Average loss: 3.0392
Iteration: 2069; Percent complete: 51.7%; Average loss: 3.3753
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.1560
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.1322
Iteration: 2072; Percent complete: 51.8%; Average loss: 2.9823
Iteration: 2073; Percent complete: 51.8%; Average loss: 3.3360
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.1469
Iteration: 2075; Percent complete: 51.9%; Average loss: 3.3523
Iteration: 2076; Percent complete: 51.9%; Average loss: 3.0009
Iteration: 2077; Percent complete: 51.9%; Average loss: 2.6932
Iteration: 2078; Percent complete: 51.9%; Average loss: 3.0407
Iteration: 2079; Percent complete: 52.0%; Average loss: 3.1417
Iteration: 2080; Percent complete: 52.0%; Average loss: 3.2250
Iteration: 2081; Percent complete: 52.0%; Average loss: 2.9668
Iteration: 2082; Percent complete: 52.0%; Average loss: 3.2116
Iteration: 2083; Percent complete: 52.1%; Average loss: 2.9716
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.2343
Iteration: 2085; Percent complete: 52.1%; Average loss: 3.3499
Iteration: 2086; Percent complete: 52.1%; Average loss: 2.9591
Iteration: 2087; Percent complete: 52.2%; Average loss: 3.0352
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.2105
Iteration: 2089; Percent complete: 52.2%; Average loss: 2.9481
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.1714
Iteration: 2091; Percent complete: 52.3%; Average loss: 3.1111
Iteration: 2092; Percent complete: 52.3%; Average loss: 3.1457
Iteration: 2093; Percent complete: 52.3%; Average loss: 2.9128
Iteration: 2094; Percent complete: 52.3%; Average loss: 3.0929
Iteration: 2095; Percent complete: 52.4%; Average loss: 3.0103
Iteration: 2096; Percent complete: 52.4%; Average loss: 3.1010
Iteration: 2097; Percent complete: 52.4%; Average loss: 2.9531
Iteration: 2098; Percent complete: 52.4%; Average loss: 2.9243
Iteration: 2099; Percent complete: 52.5%; Average loss: 3.2966
Iteration: 2100; Percent complete: 52.5%; Average loss: 3.1417
Iteration: 2101; Percent complete: 52.5%; Average loss: 3.3295
Iteration: 2102; Percent complete: 52.5%; Average loss: 3.1202
Iteration: 2103; Percent complete: 52.6%; Average loss: 3.3037
Iteration: 2104; Percent complete: 52.6%; Average loss: 3.1487
Iteration: 2105; Percent complete: 52.6%; Average loss: 3.2911
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.3153
Iteration: 2107; Percent complete: 52.7%; Average loss: 2.9958
Iteration: 2108; Percent complete: 52.7%; Average loss: 2.9870
Iteration: 2109; Percent complete: 52.7%; Average loss: 3.0156
Iteration: 2110; Percent complete: 52.8%; Average loss: 3.3127
Iteration: 2111; Percent complete: 52.8%; Average loss: 3.2156
Iteration: 2112; Percent complete: 52.8%; Average loss: 3.0972
Iteration: 2113; Percent complete: 52.8%; Average loss: 3.2435
Iteration: 2114; Percent complete: 52.8%; Average loss: 3.0357
Iteration: 2115; Percent complete: 52.9%; Average loss: 3.0709
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.0514
Iteration: 2117; Percent complete: 52.9%; Average loss: 3.0478
Iteration: 2118; Percent complete: 52.9%; Average loss: 3.1046
Iteration: 2119; Percent complete: 53.0%; Average loss: 2.8700
Iteration: 2120; Percent complete: 53.0%; Average loss: 3.2792
Iteration: 2121; Percent complete: 53.0%; Average loss: 3.2758
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.0589
Iteration: 2123; Percent complete: 53.1%; Average loss: 3.2456
Iteration: 2124; Percent complete: 53.1%; Average loss: 2.9631
Iteration: 2125; Percent complete: 53.1%; Average loss: 2.9581
Iteration: 2126; Percent complete: 53.1%; Average loss: 2.9483
Iteration: 2127; Percent complete: 53.2%; Average loss: 2.8933
Iteration: 2128; Percent complete: 53.2%; Average loss: 3.3896
Iteration: 2129; Percent complete: 53.2%; Average loss: 3.0527
Iteration: 2130; Percent complete: 53.2%; Average loss: 3.1473
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.0156
Iteration: 2132; Percent complete: 53.3%; Average loss: 3.1822
Iteration: 2133; Percent complete: 53.3%; Average loss: 3.3073
Iteration: 2134; Percent complete: 53.3%; Average loss: 3.3098
Iteration: 2135; Percent complete: 53.4%; Average loss: 3.0833
Iteration: 2136; Percent complete: 53.4%; Average loss: 2.8954
Iteration: 2137; Percent complete: 53.4%; Average loss: 2.9852
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.2191
Iteration: 2139; Percent complete: 53.5%; Average loss: 3.1385
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.0215
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.1899
Iteration: 2142; Percent complete: 53.5%; Average loss: 3.1152
Iteration: 2143; Percent complete: 53.6%; Average loss: 3.3517
Iteration: 2144; Percent complete: 53.6%; Average loss: 2.7811
Iteration: 2145; Percent complete: 53.6%; Average loss: 2.9934
Iteration: 2146; Percent complete: 53.6%; Average loss: 3.0162
Iteration: 2147; Percent complete: 53.7%; Average loss: 3.3597
Iteration: 2148; Percent complete: 53.7%; Average loss: 2.9898
Iteration: 2149; Percent complete: 53.7%; Average loss: 3.3198
Iteration: 2150; Percent complete: 53.8%; Average loss: 3.2411
Iteration: 2151; Percent complete: 53.8%; Average loss: 2.9746
Iteration: 2152; Percent complete: 53.8%; Average loss: 3.0389
Iteration: 2153; Percent complete: 53.8%; Average loss: 3.0931
Iteration: 2154; Percent complete: 53.8%; Average loss: 3.1343
Iteration: 2155; Percent complete: 53.9%; Average loss: 3.0505
Iteration: 2156; Percent complete: 53.9%; Average loss: 2.9402
Iteration: 2157; Percent complete: 53.9%; Average loss: 3.2490
Iteration: 2158; Percent complete: 53.9%; Average loss: 3.2021
Iteration: 2159; Percent complete: 54.0%; Average loss: 3.1020
Iteration: 2160; Percent complete: 54.0%; Average loss: 3.1099
Iteration: 2161; Percent complete: 54.0%; Average loss: 3.4173
Iteration: 2162; Percent complete: 54.0%; Average loss: 2.9084
Iteration: 2163; Percent complete: 54.1%; Average loss: 3.1153
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.0700
Iteration: 2165; Percent complete: 54.1%; Average loss: 2.9050
Iteration: 2166; Percent complete: 54.1%; Average loss: 3.0159
Iteration: 2167; Percent complete: 54.2%; Average loss: 3.1693
Iteration: 2168; Percent complete: 54.2%; Average loss: 3.1725
Iteration: 2169; Percent complete: 54.2%; Average loss: 3.0698
Iteration: 2170; Percent complete: 54.2%; Average loss: 3.2354
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.0926
Iteration: 2172; Percent complete: 54.3%; Average loss: 2.9700
Iteration: 2173; Percent complete: 54.3%; Average loss: 2.9403
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.3686
Iteration: 2175; Percent complete: 54.4%; Average loss: 2.9053
Iteration: 2176; Percent complete: 54.4%; Average loss: 2.9476
Iteration: 2177; Percent complete: 54.4%; Average loss: 3.1843
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.2771
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.3100
Iteration: 2180; Percent complete: 54.5%; Average loss: 3.2032
Iteration: 2181; Percent complete: 54.5%; Average loss: 2.9603
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.0518
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.0412
Iteration: 2184; Percent complete: 54.6%; Average loss: 3.2710
Iteration: 2185; Percent complete: 54.6%; Average loss: 3.1195
Iteration: 2186; Percent complete: 54.6%; Average loss: 3.2101
Iteration: 2187; Percent complete: 54.7%; Average loss: 3.1377
Iteration: 2188; Percent complete: 54.7%; Average loss: 3.4730
Iteration: 2189; Percent complete: 54.7%; Average loss: 3.0532
Iteration: 2190; Percent complete: 54.8%; Average loss: 3.1165
Iteration: 2191; Percent complete: 54.8%; Average loss: 3.0600
Iteration: 2192; Percent complete: 54.8%; Average loss: 3.1438
Iteration: 2193; Percent complete: 54.8%; Average loss: 3.0959
Iteration: 2194; Percent complete: 54.9%; Average loss: 3.0969
Iteration: 2195; Percent complete: 54.9%; Average loss: 2.8145
Iteration: 2196; Percent complete: 54.9%; Average loss: 3.0373
Iteration: 2197; Percent complete: 54.9%; Average loss: 3.2390
Iteration: 2198; Percent complete: 54.9%; Average loss: 2.9537
Iteration: 2199; Percent complete: 55.0%; Average loss: 2.9597
Iteration: 2200; Percent complete: 55.0%; Average loss: 3.1666
Iteration: 2201; Percent complete: 55.0%; Average loss: 2.9326
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.1403
Iteration: 2203; Percent complete: 55.1%; Average loss: 3.0167
Iteration: 2204; Percent complete: 55.1%; Average loss: 2.8287
Iteration: 2205; Percent complete: 55.1%; Average loss: 2.9819
Iteration: 2206; Percent complete: 55.1%; Average loss: 3.1089
Iteration: 2207; Percent complete: 55.2%; Average loss: 3.1121
Iteration: 2208; Percent complete: 55.2%; Average loss: 3.0605
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.0488
Iteration: 2210; Percent complete: 55.2%; Average loss: 3.1895
Iteration: 2211; Percent complete: 55.3%; Average loss: 3.3101
Iteration: 2212; Percent complete: 55.3%; Average loss: 3.3496
Iteration: 2213; Percent complete: 55.3%; Average loss: 3.3596
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.2576
Iteration: 2215; Percent complete: 55.4%; Average loss: 3.0497
Iteration: 2216; Percent complete: 55.4%; Average loss: 3.1321
Iteration: 2217; Percent complete: 55.4%; Average loss: 3.1407
Iteration: 2218; Percent complete: 55.5%; Average loss: 3.0426
Iteration: 2219; Percent complete: 55.5%; Average loss: 3.3987
Iteration: 2220; Percent complete: 55.5%; Average loss: 2.8364
Iteration: 2221; Percent complete: 55.5%; Average loss: 3.0770
Iteration: 2222; Percent complete: 55.5%; Average loss: 2.9968
Iteration: 2223; Percent complete: 55.6%; Average loss: 3.1563
Iteration: 2224; Percent complete: 55.6%; Average loss: 3.0387
Iteration: 2225; Percent complete: 55.6%; Average loss: 3.0254
Iteration: 2226; Percent complete: 55.6%; Average loss: 3.0049
Iteration: 2227; Percent complete: 55.7%; Average loss: 3.0527
Iteration: 2228; Percent complete: 55.7%; Average loss: 3.2663
Iteration: 2229; Percent complete: 55.7%; Average loss: 3.0270
Iteration: 2230; Percent complete: 55.8%; Average loss: 3.2324
Iteration: 2231; Percent complete: 55.8%; Average loss: 2.8287
Iteration: 2232; Percent complete: 55.8%; Average loss: 3.0560
Iteration: 2233; Percent complete: 55.8%; Average loss: 3.1873
Iteration: 2234; Percent complete: 55.9%; Average loss: 2.9729
Iteration: 2235; Percent complete: 55.9%; Average loss: 3.2049
Iteration: 2236; Percent complete: 55.9%; Average loss: 3.1689
Iteration: 2237; Percent complete: 55.9%; Average loss: 3.4161
Iteration: 2238; Percent complete: 56.0%; Average loss: 2.9442
Iteration: 2239; Percent complete: 56.0%; Average loss: 3.1502
Iteration: 2240; Percent complete: 56.0%; Average loss: 3.1993
Iteration: 2241; Percent complete: 56.0%; Average loss: 3.0137
Iteration: 2242; Percent complete: 56.0%; Average loss: 2.9541
Iteration: 2243; Percent complete: 56.1%; Average loss: 3.1189
Iteration: 2244; Percent complete: 56.1%; Average loss: 2.9868
Iteration: 2245; Percent complete: 56.1%; Average loss: 3.1264
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.0723
Iteration: 2247; Percent complete: 56.2%; Average loss: 3.1835
Iteration: 2248; Percent complete: 56.2%; Average loss: 2.8777
Iteration: 2249; Percent complete: 56.2%; Average loss: 3.2335
Iteration: 2250; Percent complete: 56.2%; Average loss: 3.0106
Iteration: 2251; Percent complete: 56.3%; Average loss: 2.9850
Iteration: 2252; Percent complete: 56.3%; Average loss: 3.2955
Iteration: 2253; Percent complete: 56.3%; Average loss: 3.0003
Iteration: 2254; Percent complete: 56.4%; Average loss: 3.1126
Iteration: 2255; Percent complete: 56.4%; Average loss: 3.1636
Iteration: 2256; Percent complete: 56.4%; Average loss: 3.1285
Iteration: 2257; Percent complete: 56.4%; Average loss: 3.0572
Iteration: 2258; Percent complete: 56.5%; Average loss: 2.8228
Iteration: 2259; Percent complete: 56.5%; Average loss: 2.8626
Iteration: 2260; Percent complete: 56.5%; Average loss: 2.9805
Iteration: 2261; Percent complete: 56.5%; Average loss: 3.0456
Iteration: 2262; Percent complete: 56.5%; Average loss: 2.8367
Iteration: 2263; Percent complete: 56.6%; Average loss: 2.8759
Iteration: 2264; Percent complete: 56.6%; Average loss: 2.9837
Iteration: 2265; Percent complete: 56.6%; Average loss: 3.0851
Iteration: 2266; Percent complete: 56.6%; Average loss: 3.1653
Iteration: 2267; Percent complete: 56.7%; Average loss: 3.0617
Iteration: 2268; Percent complete: 56.7%; Average loss: 2.9889
Iteration: 2269; Percent complete: 56.7%; Average loss: 3.0774
Iteration: 2270; Percent complete: 56.8%; Average loss: 3.2078
Iteration: 2271; Percent complete: 56.8%; Average loss: 3.2127
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.1010
Iteration: 2273; Percent complete: 56.8%; Average loss: 3.0245
Iteration: 2274; Percent complete: 56.9%; Average loss: 2.9455
Iteration: 2275; Percent complete: 56.9%; Average loss: 3.0520
Iteration: 2276; Percent complete: 56.9%; Average loss: 2.9678
Iteration: 2277; Percent complete: 56.9%; Average loss: 3.0796
Iteration: 2278; Percent complete: 57.0%; Average loss: 3.0992
Iteration: 2279; Percent complete: 57.0%; Average loss: 3.0090
Iteration: 2280; Percent complete: 57.0%; Average loss: 2.9160
Iteration: 2281; Percent complete: 57.0%; Average loss: 3.2057
Iteration: 2282; Percent complete: 57.0%; Average loss: 3.1014
Iteration: 2283; Percent complete: 57.1%; Average loss: 2.8059
Iteration: 2284; Percent complete: 57.1%; Average loss: 3.0527
Iteration: 2285; Percent complete: 57.1%; Average loss: 3.0782
Iteration: 2286; Percent complete: 57.1%; Average loss: 3.2605
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.1435
Iteration: 2288; Percent complete: 57.2%; Average loss: 3.1532
Iteration: 2289; Percent complete: 57.2%; Average loss: 2.8707
Iteration: 2290; Percent complete: 57.2%; Average loss: 3.1130
Iteration: 2291; Percent complete: 57.3%; Average loss: 3.1906
Iteration: 2292; Percent complete: 57.3%; Average loss: 3.2088
Iteration: 2293; Percent complete: 57.3%; Average loss: 2.8798
Iteration: 2294; Percent complete: 57.4%; Average loss: 2.9936
Iteration: 2295; Percent complete: 57.4%; Average loss: 3.1955
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.3928
Iteration: 2297; Percent complete: 57.4%; Average loss: 3.0229
Iteration: 2298; Percent complete: 57.5%; Average loss: 2.9794
Iteration: 2299; Percent complete: 57.5%; Average loss: 2.9282
Iteration: 2300; Percent complete: 57.5%; Average loss: 3.2487
Iteration: 2301; Percent complete: 57.5%; Average loss: 3.0531
Iteration: 2302; Percent complete: 57.6%; Average loss: 2.9544
Iteration: 2303; Percent complete: 57.6%; Average loss: 3.1736
Iteration: 2304; Percent complete: 57.6%; Average loss: 3.0329
Iteration: 2305; Percent complete: 57.6%; Average loss: 3.3533
Iteration: 2306; Percent complete: 57.6%; Average loss: 3.0192
Iteration: 2307; Percent complete: 57.7%; Average loss: 3.2215
Iteration: 2308; Percent complete: 57.7%; Average loss: 3.0431
Iteration: 2309; Percent complete: 57.7%; Average loss: 2.8706
Iteration: 2310; Percent complete: 57.8%; Average loss: 3.1186
Iteration: 2311; Percent complete: 57.8%; Average loss: 2.9128
Iteration: 2312; Percent complete: 57.8%; Average loss: 3.2567
Iteration: 2313; Percent complete: 57.8%; Average loss: 3.0871
Iteration: 2314; Percent complete: 57.9%; Average loss: 2.9221
Iteration: 2315; Percent complete: 57.9%; Average loss: 3.1215
Iteration: 2316; Percent complete: 57.9%; Average loss: 3.1095
Iteration: 2317; Percent complete: 57.9%; Average loss: 3.1669
Iteration: 2318; Percent complete: 58.0%; Average loss: 2.8530
Iteration: 2319; Percent complete: 58.0%; Average loss: 2.8486
Iteration: 2320; Percent complete: 58.0%; Average loss: 3.1702
Iteration: 2321; Percent complete: 58.0%; Average loss: 2.8993
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.1118
Iteration: 2323; Percent complete: 58.1%; Average loss: 3.0433
Iteration: 2324; Percent complete: 58.1%; Average loss: 3.3475
Iteration: 2325; Percent complete: 58.1%; Average loss: 2.9681
Iteration: 2326; Percent complete: 58.1%; Average loss: 3.0906
Iteration: 2327; Percent complete: 58.2%; Average loss: 2.9334
Iteration: 2328; Percent complete: 58.2%; Average loss: 3.0392
Iteration: 2329; Percent complete: 58.2%; Average loss: 3.0088
Iteration: 2330; Percent complete: 58.2%; Average loss: 2.9507
Iteration: 2331; Percent complete: 58.3%; Average loss: 2.7879
Iteration: 2332; Percent complete: 58.3%; Average loss: 3.0705
Iteration: 2333; Percent complete: 58.3%; Average loss: 3.0201
Iteration: 2334; Percent complete: 58.4%; Average loss: 3.1022
Iteration: 2335; Percent complete: 58.4%; Average loss: 3.0840
Iteration: 2336; Percent complete: 58.4%; Average loss: 3.2101
Iteration: 2337; Percent complete: 58.4%; Average loss: 3.1349
Iteration: 2338; Percent complete: 58.5%; Average loss: 2.8888
Iteration: 2339; Percent complete: 58.5%; Average loss: 3.0519
Iteration: 2340; Percent complete: 58.5%; Average loss: 2.9364
Iteration: 2341; Percent complete: 58.5%; Average loss: 3.0732
Iteration: 2342; Percent complete: 58.6%; Average loss: 3.1610
Iteration: 2343; Percent complete: 58.6%; Average loss: 2.8749
Iteration: 2344; Percent complete: 58.6%; Average loss: 3.0238
Iteration: 2345; Percent complete: 58.6%; Average loss: 2.9820
Iteration: 2346; Percent complete: 58.7%; Average loss: 3.3868
Iteration: 2347; Percent complete: 58.7%; Average loss: 3.1550
Iteration: 2348; Percent complete: 58.7%; Average loss: 2.8302
Iteration: 2349; Percent complete: 58.7%; Average loss: 3.0980
Iteration: 2350; Percent complete: 58.8%; Average loss: 3.0218
Iteration: 2351; Percent complete: 58.8%; Average loss: 2.7946
Iteration: 2352; Percent complete: 58.8%; Average loss: 3.3221
Iteration: 2353; Percent complete: 58.8%; Average loss: 3.1366
Iteration: 2354; Percent complete: 58.9%; Average loss: 3.0315
Iteration: 2355; Percent complete: 58.9%; Average loss: 2.9320
Iteration: 2356; Percent complete: 58.9%; Average loss: 3.0696
Iteration: 2357; Percent complete: 58.9%; Average loss: 3.1418
Iteration: 2358; Percent complete: 59.0%; Average loss: 3.1275
Iteration: 2359; Percent complete: 59.0%; Average loss: 2.9967
Iteration: 2360; Percent complete: 59.0%; Average loss: 3.2819
Iteration: 2361; Percent complete: 59.0%; Average loss: 3.0300
Iteration: 2362; Percent complete: 59.1%; Average loss: 2.9954
Iteration: 2363; Percent complete: 59.1%; Average loss: 2.9929
Iteration: 2364; Percent complete: 59.1%; Average loss: 2.9635
Iteration: 2365; Percent complete: 59.1%; Average loss: 2.9408
Iteration: 2366; Percent complete: 59.2%; Average loss: 2.9749
Iteration: 2367; Percent complete: 59.2%; Average loss: 2.9896
Iteration: 2368; Percent complete: 59.2%; Average loss: 3.2438
Iteration: 2369; Percent complete: 59.2%; Average loss: 2.9768
Iteration: 2370; Percent complete: 59.2%; Average loss: 3.0994
Iteration: 2371; Percent complete: 59.3%; Average loss: 2.9269
Iteration: 2372; Percent complete: 59.3%; Average loss: 3.0463
Iteration: 2373; Percent complete: 59.3%; Average loss: 3.2808
Iteration: 2374; Percent complete: 59.4%; Average loss: 3.0773
Iteration: 2375; Percent complete: 59.4%; Average loss: 3.0314
Iteration: 2376; Percent complete: 59.4%; Average loss: 3.1202
Iteration: 2377; Percent complete: 59.4%; Average loss: 3.0520
Iteration: 2378; Percent complete: 59.5%; Average loss: 3.0360
Iteration: 2379; Percent complete: 59.5%; Average loss: 3.1139
Iteration: 2380; Percent complete: 59.5%; Average loss: 2.9967
Iteration: 2381; Percent complete: 59.5%; Average loss: 2.9618
Iteration: 2382; Percent complete: 59.6%; Average loss: 2.7819
Iteration: 2383; Percent complete: 59.6%; Average loss: 2.9989
Iteration: 2384; Percent complete: 59.6%; Average loss: 3.0203
Iteration: 2385; Percent complete: 59.6%; Average loss: 3.3434
Iteration: 2386; Percent complete: 59.7%; Average loss: 3.2875
Iteration: 2387; Percent complete: 59.7%; Average loss: 3.2311
Iteration: 2388; Percent complete: 59.7%; Average loss: 2.9551
Iteration: 2389; Percent complete: 59.7%; Average loss: 3.1442
Iteration: 2390; Percent complete: 59.8%; Average loss: 2.9521
Iteration: 2391; Percent complete: 59.8%; Average loss: 3.0841
Iteration: 2392; Percent complete: 59.8%; Average loss: 2.8511
Iteration: 2393; Percent complete: 59.8%; Average loss: 2.9449
Iteration: 2394; Percent complete: 59.9%; Average loss: 2.9273
Iteration: 2395; Percent complete: 59.9%; Average loss: 2.8887
Iteration: 2396; Percent complete: 59.9%; Average loss: 3.0264
Iteration: 2397; Percent complete: 59.9%; Average loss: 2.9878
Iteration: 2398; Percent complete: 60.0%; Average loss: 2.9257
Iteration: 2399; Percent complete: 60.0%; Average loss: 3.1423
Iteration: 2400; Percent complete: 60.0%; Average loss: 3.1701
Iteration: 2401; Percent complete: 60.0%; Average loss: 2.9284
Iteration: 2402; Percent complete: 60.1%; Average loss: 2.7593
Iteration: 2403; Percent complete: 60.1%; Average loss: 2.8449
Iteration: 2404; Percent complete: 60.1%; Average loss: 2.9212
Iteration: 2405; Percent complete: 60.1%; Average loss: 3.1418
Iteration: 2406; Percent complete: 60.2%; Average loss: 2.9101
Iteration: 2407; Percent complete: 60.2%; Average loss: 2.9024
Iteration: 2408; Percent complete: 60.2%; Average loss: 3.1394
Iteration: 2409; Percent complete: 60.2%; Average loss: 3.0246
Iteration: 2410; Percent complete: 60.2%; Average loss: 2.9045
Iteration: 2411; Percent complete: 60.3%; Average loss: 2.8461
Iteration: 2412; Percent complete: 60.3%; Average loss: 3.1115
Iteration: 2413; Percent complete: 60.3%; Average loss: 2.9222
Iteration: 2414; Percent complete: 60.4%; Average loss: 3.2014
Iteration: 2415; Percent complete: 60.4%; Average loss: 3.0951
Iteration: 2416; Percent complete: 60.4%; Average loss: 3.0782
Iteration: 2417; Percent complete: 60.4%; Average loss: 3.0601
Iteration: 2418; Percent complete: 60.5%; Average loss: 3.2441
Iteration: 2419; Percent complete: 60.5%; Average loss: 3.1506
Iteration: 2420; Percent complete: 60.5%; Average loss: 3.2535
Iteration: 2421; Percent complete: 60.5%; Average loss: 3.1067
Iteration: 2422; Percent complete: 60.6%; Average loss: 2.9497
Iteration: 2423; Percent complete: 60.6%; Average loss: 3.2337
Iteration: 2424; Percent complete: 60.6%; Average loss: 3.2053
Iteration: 2425; Percent complete: 60.6%; Average loss: 2.7635
Iteration: 2426; Percent complete: 60.7%; Average loss: 3.2039
Iteration: 2427; Percent complete: 60.7%; Average loss: 2.9034
Iteration: 2428; Percent complete: 60.7%; Average loss: 3.2972
Iteration: 2429; Percent complete: 60.7%; Average loss: 2.7562
Iteration: 2430; Percent complete: 60.8%; Average loss: 2.8648
Iteration: 2431; Percent complete: 60.8%; Average loss: 2.9984
Iteration: 2432; Percent complete: 60.8%; Average loss: 2.9539
Iteration: 2433; Percent complete: 60.8%; Average loss: 3.0650
Iteration: 2434; Percent complete: 60.9%; Average loss: 2.8901
Iteration: 2435; Percent complete: 60.9%; Average loss: 3.0986
Iteration: 2436; Percent complete: 60.9%; Average loss: 3.0417
Iteration: 2437; Percent complete: 60.9%; Average loss: 2.9921
Iteration: 2438; Percent complete: 61.0%; Average loss: 2.8101
Iteration: 2439; Percent complete: 61.0%; Average loss: 3.0682
Iteration: 2440; Percent complete: 61.0%; Average loss: 2.9361
Iteration: 2441; Percent complete: 61.0%; Average loss: 2.7316
Iteration: 2442; Percent complete: 61.1%; Average loss: 3.1739
Iteration: 2443; Percent complete: 61.1%; Average loss: 3.2132
Iteration: 2444; Percent complete: 61.1%; Average loss: 3.1646
Iteration: 2445; Percent complete: 61.1%; Average loss: 3.0900
Iteration: 2446; Percent complete: 61.2%; Average loss: 2.8368
Iteration: 2447; Percent complete: 61.2%; Average loss: 2.9594
Iteration: 2448; Percent complete: 61.2%; Average loss: 3.0304
Iteration: 2449; Percent complete: 61.2%; Average loss: 3.0150
Iteration: 2450; Percent complete: 61.3%; Average loss: 3.1018
Iteration: 2451; Percent complete: 61.3%; Average loss: 3.0888
Iteration: 2452; Percent complete: 61.3%; Average loss: 3.1694
Iteration: 2453; Percent complete: 61.3%; Average loss: 3.0690
Iteration: 2454; Percent complete: 61.4%; Average loss: 2.9770
Iteration: 2455; Percent complete: 61.4%; Average loss: 2.9952
Iteration: 2456; Percent complete: 61.4%; Average loss: 2.9469
Iteration: 2457; Percent complete: 61.4%; Average loss: 3.0942
Iteration: 2458; Percent complete: 61.5%; Average loss: 2.9686
Iteration: 2459; Percent complete: 61.5%; Average loss: 2.9146
Iteration: 2460; Percent complete: 61.5%; Average loss: 2.9327
Iteration: 2461; Percent complete: 61.5%; Average loss: 2.8914
Iteration: 2462; Percent complete: 61.6%; Average loss: 2.9542
Iteration: 2463; Percent complete: 61.6%; Average loss: 2.9559
Iteration: 2464; Percent complete: 61.6%; Average loss: 2.9894
Iteration: 2465; Percent complete: 61.6%; Average loss: 3.0341
Iteration: 2466; Percent complete: 61.7%; Average loss: 2.9139
Iteration: 2467; Percent complete: 61.7%; Average loss: 3.3236
Iteration: 2468; Percent complete: 61.7%; Average loss: 2.9985
Iteration: 2469; Percent complete: 61.7%; Average loss: 2.6343
Iteration: 2470; Percent complete: 61.8%; Average loss: 2.9066
Iteration: 2471; Percent complete: 61.8%; Average loss: 3.1686
Iteration: 2472; Percent complete: 61.8%; Average loss: 2.8775
Iteration: 2473; Percent complete: 61.8%; Average loss: 2.9888
Iteration: 2474; Percent complete: 61.9%; Average loss: 3.0410
Iteration: 2475; Percent complete: 61.9%; Average loss: 2.8269
Iteration: 2476; Percent complete: 61.9%; Average loss: 2.9567
Iteration: 2477; Percent complete: 61.9%; Average loss: 3.0299
Iteration: 2478; Percent complete: 62.0%; Average loss: 2.8841
Iteration: 2479; Percent complete: 62.0%; Average loss: 2.9087
Iteration: 2480; Percent complete: 62.0%; Average loss: 2.8159
Iteration: 2481; Percent complete: 62.0%; Average loss: 3.0475
Iteration: 2482; Percent complete: 62.1%; Average loss: 3.0794
Iteration: 2483; Percent complete: 62.1%; Average loss: 3.0593
Iteration: 2484; Percent complete: 62.1%; Average loss: 3.2672
Iteration: 2485; Percent complete: 62.1%; Average loss: 2.8728
Iteration: 2486; Percent complete: 62.2%; Average loss: 3.1396
Iteration: 2487; Percent complete: 62.2%; Average loss: 3.1537
Iteration: 2488; Percent complete: 62.2%; Average loss: 3.0624
Iteration: 2489; Percent complete: 62.2%; Average loss: 3.0706
Iteration: 2490; Percent complete: 62.3%; Average loss: 3.2377
Iteration: 2491; Percent complete: 62.3%; Average loss: 2.9001
Iteration: 2492; Percent complete: 62.3%; Average loss: 3.2364
Iteration: 2493; Percent complete: 62.3%; Average loss: 2.7923
Iteration: 2494; Percent complete: 62.4%; Average loss: 3.0180
Iteration: 2495; Percent complete: 62.4%; Average loss: 2.7654
Iteration: 2496; Percent complete: 62.4%; Average loss: 2.7747
Iteration: 2497; Percent complete: 62.4%; Average loss: 2.8592
Iteration: 2498; Percent complete: 62.5%; Average loss: 2.9318
Iteration: 2499; Percent complete: 62.5%; Average loss: 3.0473
Iteration: 2500; Percent complete: 62.5%; Average loss: 3.0576
Iteration: 2501; Percent complete: 62.5%; Average loss: 2.9241
Iteration: 2502; Percent complete: 62.5%; Average loss: 2.9804
Iteration: 2503; Percent complete: 62.6%; Average loss: 3.0283
Iteration: 2504; Percent complete: 62.6%; Average loss: 3.0646
Iteration: 2505; Percent complete: 62.6%; Average loss: 2.9554
Iteration: 2506; Percent complete: 62.6%; Average loss: 2.7462
Iteration: 2507; Percent complete: 62.7%; Average loss: 3.0737
Iteration: 2508; Percent complete: 62.7%; Average loss: 3.1555
Iteration: 2509; Percent complete: 62.7%; Average loss: 3.1570
Iteration: 2510; Percent complete: 62.7%; Average loss: 2.9717
Iteration: 2511; Percent complete: 62.8%; Average loss: 2.7863
Iteration: 2512; Percent complete: 62.8%; Average loss: 3.0537
Iteration: 2513; Percent complete: 62.8%; Average loss: 2.7418
Iteration: 2514; Percent complete: 62.8%; Average loss: 3.0220
Iteration: 2515; Percent complete: 62.9%; Average loss: 2.7838
Iteration: 2516; Percent complete: 62.9%; Average loss: 3.0561
Iteration: 2517; Percent complete: 62.9%; Average loss: 3.1309
Iteration: 2518; Percent complete: 62.9%; Average loss: 2.8252
Iteration: 2519; Percent complete: 63.0%; Average loss: 3.2442
Iteration: 2520; Percent complete: 63.0%; Average loss: 2.9520
Iteration: 2521; Percent complete: 63.0%; Average loss: 3.2025
Iteration: 2522; Percent complete: 63.0%; Average loss: 3.0195
Iteration: 2523; Percent complete: 63.1%; Average loss: 2.8596
Iteration: 2524; Percent complete: 63.1%; Average loss: 2.9091
Iteration: 2525; Percent complete: 63.1%; Average loss: 2.8401
Iteration: 2526; Percent complete: 63.1%; Average loss: 2.8673
Iteration: 2527; Percent complete: 63.2%; Average loss: 3.0792
Iteration: 2528; Percent complete: 63.2%; Average loss: 2.9006
Iteration: 2529; Percent complete: 63.2%; Average loss: 2.6640
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.0948
Iteration: 2531; Percent complete: 63.3%; Average loss: 3.0779
Iteration: 2532; Percent complete: 63.3%; Average loss: 2.9993
Iteration: 2533; Percent complete: 63.3%; Average loss: 3.0178
Iteration: 2534; Percent complete: 63.3%; Average loss: 3.1980
Iteration: 2535; Percent complete: 63.4%; Average loss: 3.0509
Iteration: 2536; Percent complete: 63.4%; Average loss: 2.9506
Iteration: 2537; Percent complete: 63.4%; Average loss: 2.9267
Iteration: 2538; Percent complete: 63.4%; Average loss: 3.0905
Iteration: 2539; Percent complete: 63.5%; Average loss: 2.8461
Iteration: 2540; Percent complete: 63.5%; Average loss: 3.0504
Iteration: 2541; Percent complete: 63.5%; Average loss: 3.1476
Iteration: 2542; Percent complete: 63.5%; Average loss: 2.9927
Iteration: 2543; Percent complete: 63.6%; Average loss: 3.0478
Iteration: 2544; Percent complete: 63.6%; Average loss: 2.8634
Iteration: 2545; Percent complete: 63.6%; Average loss: 3.0107
Iteration: 2546; Percent complete: 63.6%; Average loss: 2.9204
Iteration: 2547; Percent complete: 63.7%; Average loss: 3.1204
Iteration: 2548; Percent complete: 63.7%; Average loss: 3.0723
Iteration: 2549; Percent complete: 63.7%; Average loss: 2.9693
Iteration: 2550; Percent complete: 63.7%; Average loss: 3.0645
Iteration: 2551; Percent complete: 63.8%; Average loss: 3.0285
Iteration: 2552; Percent complete: 63.8%; Average loss: 3.2100
Iteration: 2553; Percent complete: 63.8%; Average loss: 3.0451
Iteration: 2554; Percent complete: 63.8%; Average loss: 2.9521
Iteration: 2555; Percent complete: 63.9%; Average loss: 3.2409
Iteration: 2556; Percent complete: 63.9%; Average loss: 3.0228
Iteration: 2557; Percent complete: 63.9%; Average loss: 2.8337
Iteration: 2558; Percent complete: 63.9%; Average loss: 3.0562
Iteration: 2559; Percent complete: 64.0%; Average loss: 3.1473
Iteration: 2560; Percent complete: 64.0%; Average loss: 2.9424
Iteration: 2561; Percent complete: 64.0%; Average loss: 2.9761
Iteration: 2562; Percent complete: 64.0%; Average loss: 2.9287
Iteration: 2563; Percent complete: 64.1%; Average loss: 3.1551
Iteration: 2564; Percent complete: 64.1%; Average loss: 3.0636
Iteration: 2565; Percent complete: 64.1%; Average loss: 2.9113
Iteration: 2566; Percent complete: 64.1%; Average loss: 3.1222
Iteration: 2567; Percent complete: 64.2%; Average loss: 2.7876
Iteration: 2568; Percent complete: 64.2%; Average loss: 2.9315
Iteration: 2569; Percent complete: 64.2%; Average loss: 3.0533
Iteration: 2570; Percent complete: 64.2%; Average loss: 2.9444
Iteration: 2571; Percent complete: 64.3%; Average loss: 3.2556
Iteration: 2572; Percent complete: 64.3%; Average loss: 3.0672
Iteration: 2573; Percent complete: 64.3%; Average loss: 3.0667
Iteration: 2574; Percent complete: 64.3%; Average loss: 3.0091
Iteration: 2575; Percent complete: 64.4%; Average loss: 2.9556
Iteration: 2576; Percent complete: 64.4%; Average loss: 2.8845
Iteration: 2577; Percent complete: 64.4%; Average loss: 2.6987
Iteration: 2578; Percent complete: 64.5%; Average loss: 3.0196
Iteration: 2579; Percent complete: 64.5%; Average loss: 3.0767
Iteration: 2580; Percent complete: 64.5%; Average loss: 2.8023
Iteration: 2581; Percent complete: 64.5%; Average loss: 2.7830
Iteration: 2582; Percent complete: 64.5%; Average loss: 2.7071
Iteration: 2583; Percent complete: 64.6%; Average loss: 2.8795
Iteration: 2584; Percent complete: 64.6%; Average loss: 3.2134
Iteration: 2585; Percent complete: 64.6%; Average loss: 2.9481
Iteration: 2586; Percent complete: 64.6%; Average loss: 3.0247
Iteration: 2587; Percent complete: 64.7%; Average loss: 3.1587
Iteration: 2588; Percent complete: 64.7%; Average loss: 2.9070
Iteration: 2589; Percent complete: 64.7%; Average loss: 3.2899
Iteration: 2590; Percent complete: 64.8%; Average loss: 2.9454
Iteration: 2591; Percent complete: 64.8%; Average loss: 2.8136
Iteration: 2592; Percent complete: 64.8%; Average loss: 3.0366
Iteration: 2593; Percent complete: 64.8%; Average loss: 2.5703
Iteration: 2594; Percent complete: 64.8%; Average loss: 2.8273
Iteration: 2595; Percent complete: 64.9%; Average loss: 3.0167
Iteration: 2596; Percent complete: 64.9%; Average loss: 2.8472
Iteration: 2597; Percent complete: 64.9%; Average loss: 3.0658
Iteration: 2598; Percent complete: 65.0%; Average loss: 3.0306
Iteration: 2599; Percent complete: 65.0%; Average loss: 2.8032
Iteration: 2600; Percent complete: 65.0%; Average loss: 2.8881
Iteration: 2601; Percent complete: 65.0%; Average loss: 2.7904
Iteration: 2602; Percent complete: 65.0%; Average loss: 3.0795
Iteration: 2603; Percent complete: 65.1%; Average loss: 2.9057
Iteration: 2604; Percent complete: 65.1%; Average loss: 3.0995
Iteration: 2605; Percent complete: 65.1%; Average loss: 2.8895
Iteration: 2606; Percent complete: 65.1%; Average loss: 2.8967
Iteration: 2607; Percent complete: 65.2%; Average loss: 2.9735
Iteration: 2608; Percent complete: 65.2%; Average loss: 3.1039
Iteration: 2609; Percent complete: 65.2%; Average loss: 2.8566
Iteration: 2610; Percent complete: 65.2%; Average loss: 3.1939
Iteration: 2611; Percent complete: 65.3%; Average loss: 2.9628
Iteration: 2612; Percent complete: 65.3%; Average loss: 2.9442
Iteration: 2613; Percent complete: 65.3%; Average loss: 2.8257
Iteration: 2614; Percent complete: 65.3%; Average loss: 2.9827
Iteration: 2615; Percent complete: 65.4%; Average loss: 3.0010
Iteration: 2616; Percent complete: 65.4%; Average loss: 2.9977
Iteration: 2617; Percent complete: 65.4%; Average loss: 3.1017
Iteration: 2618; Percent complete: 65.5%; Average loss: 2.8879
Iteration: 2619; Percent complete: 65.5%; Average loss: 2.9733
Iteration: 2620; Percent complete: 65.5%; Average loss: 2.7203
Iteration: 2621; Percent complete: 65.5%; Average loss: 2.9087
Iteration: 2622; Percent complete: 65.5%; Average loss: 2.8482
Iteration: 2623; Percent complete: 65.6%; Average loss: 3.2892
Iteration: 2624; Percent complete: 65.6%; Average loss: 2.9727
Iteration: 2625; Percent complete: 65.6%; Average loss: 2.7637
Iteration: 2626; Percent complete: 65.6%; Average loss: 2.9604
Iteration: 2627; Percent complete: 65.7%; Average loss: 3.0745
Iteration: 2628; Percent complete: 65.7%; Average loss: 2.9584
Iteration: 2629; Percent complete: 65.7%; Average loss: 2.8962
Iteration: 2630; Percent complete: 65.8%; Average loss: 3.3467
Iteration: 2631; Percent complete: 65.8%; Average loss: 3.0789
Iteration: 2632; Percent complete: 65.8%; Average loss: 2.9158
Iteration: 2633; Percent complete: 65.8%; Average loss: 3.1186
Iteration: 2634; Percent complete: 65.8%; Average loss: 2.8684
Iteration: 2635; Percent complete: 65.9%; Average loss: 3.0896
Iteration: 2636; Percent complete: 65.9%; Average loss: 2.8350
Iteration: 2637; Percent complete: 65.9%; Average loss: 2.8798
Iteration: 2638; Percent complete: 66.0%; Average loss: 2.7807
Iteration: 2639; Percent complete: 66.0%; Average loss: 2.8111
Iteration: 2640; Percent complete: 66.0%; Average loss: 2.8423
Iteration: 2641; Percent complete: 66.0%; Average loss: 3.0574
Iteration: 2642; Percent complete: 66.0%; Average loss: 3.1598
Iteration: 2643; Percent complete: 66.1%; Average loss: 3.2607
Iteration: 2644; Percent complete: 66.1%; Average loss: 3.1466
Iteration: 2645; Percent complete: 66.1%; Average loss: 3.1606
Iteration: 2646; Percent complete: 66.1%; Average loss: 3.1034
Iteration: 2647; Percent complete: 66.2%; Average loss: 2.7160
Iteration: 2648; Percent complete: 66.2%; Average loss: 2.9219
Iteration: 2649; Percent complete: 66.2%; Average loss: 3.0273
Iteration: 2650; Percent complete: 66.2%; Average loss: 2.9300
Iteration: 2651; Percent complete: 66.3%; Average loss: 2.9106
Iteration: 2652; Percent complete: 66.3%; Average loss: 2.8372
Iteration: 2653; Percent complete: 66.3%; Average loss: 2.8374
Iteration: 2654; Percent complete: 66.3%; Average loss: 2.7603
Iteration: 2655; Percent complete: 66.4%; Average loss: 3.1465
Iteration: 2656; Percent complete: 66.4%; Average loss: 2.8844
Iteration: 2657; Percent complete: 66.4%; Average loss: 3.1226
Iteration: 2658; Percent complete: 66.5%; Average loss: 3.0492
Iteration: 2659; Percent complete: 66.5%; Average loss: 2.8980
Iteration: 2660; Percent complete: 66.5%; Average loss: 2.9525
Iteration: 2661; Percent complete: 66.5%; Average loss: 2.9754
Iteration: 2662; Percent complete: 66.5%; Average loss: 2.9477
Iteration: 2663; Percent complete: 66.6%; Average loss: 2.8081
Iteration: 2664; Percent complete: 66.6%; Average loss: 3.0122
Iteration: 2665; Percent complete: 66.6%; Average loss: 2.8769
Iteration: 2666; Percent complete: 66.6%; Average loss: 2.9538
Iteration: 2667; Percent complete: 66.7%; Average loss: 3.0542
Iteration: 2668; Percent complete: 66.7%; Average loss: 2.8875
Iteration: 2669; Percent complete: 66.7%; Average loss: 2.8909
Iteration: 2670; Percent complete: 66.8%; Average loss: 3.1265
Iteration: 2671; Percent complete: 66.8%; Average loss: 2.8677
Iteration: 2672; Percent complete: 66.8%; Average loss: 3.0160
Iteration: 2673; Percent complete: 66.8%; Average loss: 3.0971
Iteration: 2674; Percent complete: 66.8%; Average loss: 3.1535
Iteration: 2675; Percent complete: 66.9%; Average loss: 3.0300
Iteration: 2676; Percent complete: 66.9%; Average loss: 2.9224
Iteration: 2677; Percent complete: 66.9%; Average loss: 3.1246
Iteration: 2678; Percent complete: 67.0%; Average loss: 3.0214
Iteration: 2679; Percent complete: 67.0%; Average loss: 3.1897
Iteration: 2680; Percent complete: 67.0%; Average loss: 3.0588
Iteration: 2681; Percent complete: 67.0%; Average loss: 2.7691
Iteration: 2682; Percent complete: 67.0%; Average loss: 3.0261
Iteration: 2683; Percent complete: 67.1%; Average loss: 3.0855
Iteration: 2684; Percent complete: 67.1%; Average loss: 3.0010
Iteration: 2685; Percent complete: 67.1%; Average loss: 3.0198
Iteration: 2686; Percent complete: 67.2%; Average loss: 2.8633
Iteration: 2687; Percent complete: 67.2%; Average loss: 2.8910
Iteration: 2688; Percent complete: 67.2%; Average loss: 3.0280
Iteration: 2689; Percent complete: 67.2%; Average loss: 2.8338
Iteration: 2690; Percent complete: 67.2%; Average loss: 3.0960
Iteration: 2691; Percent complete: 67.3%; Average loss: 3.0258
Iteration: 2692; Percent complete: 67.3%; Average loss: 3.0061
Iteration: 2693; Percent complete: 67.3%; Average loss: 2.9936
Iteration: 2694; Percent complete: 67.3%; Average loss: 3.3403
Iteration: 2695; Percent complete: 67.4%; Average loss: 2.7447
Iteration: 2696; Percent complete: 67.4%; Average loss: 3.1001
Iteration: 2697; Percent complete: 67.4%; Average loss: 2.9011
Iteration: 2698; Percent complete: 67.5%; Average loss: 2.9200
Iteration: 2699; Percent complete: 67.5%; Average loss: 3.1799
Iteration: 2700; Percent complete: 67.5%; Average loss: 3.1779
Iteration: 2701; Percent complete: 67.5%; Average loss: 2.8901
Iteration: 2702; Percent complete: 67.5%; Average loss: 2.8164
Iteration: 2703; Percent complete: 67.6%; Average loss: 2.9854
Iteration: 2704; Percent complete: 67.6%; Average loss: 2.9815
Iteration: 2705; Percent complete: 67.6%; Average loss: 2.8225
Iteration: 2706; Percent complete: 67.7%; Average loss: 2.8493
Iteration: 2707; Percent complete: 67.7%; Average loss: 2.9588
Iteration: 2708; Percent complete: 67.7%; Average loss: 2.8902
Iteration: 2709; Percent complete: 67.7%; Average loss: 2.9325
Iteration: 2710; Percent complete: 67.8%; Average loss: 2.8661
Iteration: 2711; Percent complete: 67.8%; Average loss: 3.1089
Iteration: 2712; Percent complete: 67.8%; Average loss: 2.8419
Iteration: 2713; Percent complete: 67.8%; Average loss: 3.2994
Iteration: 2714; Percent complete: 67.8%; Average loss: 2.9559
Iteration: 2715; Percent complete: 67.9%; Average loss: 2.7577
Iteration: 2716; Percent complete: 67.9%; Average loss: 2.7435
Iteration: 2717; Percent complete: 67.9%; Average loss: 3.1989
Iteration: 2718; Percent complete: 68.0%; Average loss: 2.8561
Iteration: 2719; Percent complete: 68.0%; Average loss: 2.9701
Iteration: 2720; Percent complete: 68.0%; Average loss: 2.8294
Iteration: 2721; Percent complete: 68.0%; Average loss: 3.1696
Iteration: 2722; Percent complete: 68.0%; Average loss: 2.8482
Iteration: 2723; Percent complete: 68.1%; Average loss: 3.2670
Iteration: 2724; Percent complete: 68.1%; Average loss: 2.9122
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.9368
Iteration: 2726; Percent complete: 68.2%; Average loss: 2.8947
Iteration: 2727; Percent complete: 68.2%; Average loss: 2.7283
Iteration: 2728; Percent complete: 68.2%; Average loss: 2.9719
Iteration: 2729; Percent complete: 68.2%; Average loss: 3.0061
Iteration: 2730; Percent complete: 68.2%; Average loss: 2.7835
Iteration: 2731; Percent complete: 68.3%; Average loss: 3.0044
Iteration: 2732; Percent complete: 68.3%; Average loss: 2.8675
Iteration: 2733; Percent complete: 68.3%; Average loss: 2.8624
Iteration: 2734; Percent complete: 68.3%; Average loss: 2.8453
Iteration: 2735; Percent complete: 68.4%; Average loss: 2.9751
Iteration: 2736; Percent complete: 68.4%; Average loss: 3.0946
Iteration: 2737; Percent complete: 68.4%; Average loss: 2.6779
Iteration: 2738; Percent complete: 68.5%; Average loss: 3.2069
Iteration: 2739; Percent complete: 68.5%; Average loss: 2.7680
Iteration: 2740; Percent complete: 68.5%; Average loss: 2.8870
Iteration: 2741; Percent complete: 68.5%; Average loss: 3.0212
Iteration: 2742; Percent complete: 68.5%; Average loss: 3.1491
Iteration: 2743; Percent complete: 68.6%; Average loss: 3.0674
Iteration: 2744; Percent complete: 68.6%; Average loss: 2.8804
Iteration: 2745; Percent complete: 68.6%; Average loss: 3.1009
Iteration: 2746; Percent complete: 68.7%; Average loss: 2.9992
Iteration: 2747; Percent complete: 68.7%; Average loss: 2.7605
Iteration: 2748; Percent complete: 68.7%; Average loss: 2.8196
Iteration: 2749; Percent complete: 68.7%; Average loss: 2.9295
Iteration: 2750; Percent complete: 68.8%; Average loss: 3.1567
Iteration: 2751; Percent complete: 68.8%; Average loss: 2.9678
Iteration: 2752; Percent complete: 68.8%; Average loss: 2.8988
Iteration: 2753; Percent complete: 68.8%; Average loss: 2.7740
Iteration: 2754; Percent complete: 68.8%; Average loss: 2.7979
Iteration: 2755; Percent complete: 68.9%; Average loss: 3.0103
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.8870
Iteration: 2757; Percent complete: 68.9%; Average loss: 2.8074
Iteration: 2758; Percent complete: 69.0%; Average loss: 3.0456
Iteration: 2759; Percent complete: 69.0%; Average loss: 2.9426
Iteration: 2760; Percent complete: 69.0%; Average loss: 2.9389
Iteration: 2761; Percent complete: 69.0%; Average loss: 3.0373
Iteration: 2762; Percent complete: 69.0%; Average loss: 2.9291
Iteration: 2763; Percent complete: 69.1%; Average loss: 3.0064
Iteration: 2764; Percent complete: 69.1%; Average loss: 2.8484
Iteration: 2765; Percent complete: 69.1%; Average loss: 3.1376
Iteration: 2766; Percent complete: 69.2%; Average loss: 3.0311
Iteration: 2767; Percent complete: 69.2%; Average loss: 2.8944
Iteration: 2768; Percent complete: 69.2%; Average loss: 2.7138
Iteration: 2769; Percent complete: 69.2%; Average loss: 3.0131
Iteration: 2770; Percent complete: 69.2%; Average loss: 2.9380
Iteration: 2771; Percent complete: 69.3%; Average loss: 2.9581
Iteration: 2772; Percent complete: 69.3%; Average loss: 3.0384
Iteration: 2773; Percent complete: 69.3%; Average loss: 2.8438
Iteration: 2774; Percent complete: 69.3%; Average loss: 2.8641
Iteration: 2775; Percent complete: 69.4%; Average loss: 2.7228
Iteration: 2776; Percent complete: 69.4%; Average loss: 3.0221
Iteration: 2777; Percent complete: 69.4%; Average loss: 2.9769
Iteration: 2778; Percent complete: 69.5%; Average loss: 3.2075
Iteration: 2779; Percent complete: 69.5%; Average loss: 2.8972
Iteration: 2780; Percent complete: 69.5%; Average loss: 2.6597
Iteration: 2781; Percent complete: 69.5%; Average loss: 3.0082
Iteration: 2782; Percent complete: 69.5%; Average loss: 3.0473
Iteration: 2783; Percent complete: 69.6%; Average loss: 2.9418
Iteration: 2784; Percent complete: 69.6%; Average loss: 2.8172
Iteration: 2785; Percent complete: 69.6%; Average loss: 2.7362
Iteration: 2786; Percent complete: 69.7%; Average loss: 2.8440
Iteration: 2787; Percent complete: 69.7%; Average loss: 2.9177
Iteration: 2788; Percent complete: 69.7%; Average loss: 2.9245
Iteration: 2789; Percent complete: 69.7%; Average loss: 3.0849
Iteration: 2790; Percent complete: 69.8%; Average loss: 3.1445
Iteration: 2791; Percent complete: 69.8%; Average loss: 2.8941
Iteration: 2792; Percent complete: 69.8%; Average loss: 3.1600
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.9483
Iteration: 2794; Percent complete: 69.8%; Average loss: 2.8399
Iteration: 2795; Percent complete: 69.9%; Average loss: 2.8083
Iteration: 2796; Percent complete: 69.9%; Average loss: 2.9996
Iteration: 2797; Percent complete: 69.9%; Average loss: 2.8978
Iteration: 2798; Percent complete: 70.0%; Average loss: 2.8737
Iteration: 2799; Percent complete: 70.0%; Average loss: 2.9232
Iteration: 2800; Percent complete: 70.0%; Average loss: 3.0348
Iteration: 2801; Percent complete: 70.0%; Average loss: 2.8300
Iteration: 2802; Percent complete: 70.0%; Average loss: 3.1638
Iteration: 2803; Percent complete: 70.1%; Average loss: 2.5473
Iteration: 2804; Percent complete: 70.1%; Average loss: 2.6906
Iteration: 2805; Percent complete: 70.1%; Average loss: 2.8880
Iteration: 2806; Percent complete: 70.2%; Average loss: 2.9540
Iteration: 2807; Percent complete: 70.2%; Average loss: 3.0918
Iteration: 2808; Percent complete: 70.2%; Average loss: 2.8520
Iteration: 2809; Percent complete: 70.2%; Average loss: 3.0633
Iteration: 2810; Percent complete: 70.2%; Average loss: 2.9104
Iteration: 2811; Percent complete: 70.3%; Average loss: 2.8707
Iteration: 2812; Percent complete: 70.3%; Average loss: 2.7545
Iteration: 2813; Percent complete: 70.3%; Average loss: 2.7544
Iteration: 2814; Percent complete: 70.3%; Average loss: 2.8373
Iteration: 2815; Percent complete: 70.4%; Average loss: 3.0952
Iteration: 2816; Percent complete: 70.4%; Average loss: 2.9903
Iteration: 2817; Percent complete: 70.4%; Average loss: 2.8851
Iteration: 2818; Percent complete: 70.5%; Average loss: 2.8164
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.9701
Iteration: 2820; Percent complete: 70.5%; Average loss: 3.0064
Iteration: 2821; Percent complete: 70.5%; Average loss: 2.8925
Iteration: 2822; Percent complete: 70.5%; Average loss: 2.8993
Iteration: 2823; Percent complete: 70.6%; Average loss: 3.1257
Iteration: 2824; Percent complete: 70.6%; Average loss: 2.9810
Iteration: 2825; Percent complete: 70.6%; Average loss: 3.0388
Iteration: 2826; Percent complete: 70.7%; Average loss: 3.1982
Iteration: 2827; Percent complete: 70.7%; Average loss: 3.0193
Iteration: 2828; Percent complete: 70.7%; Average loss: 2.9325
Iteration: 2829; Percent complete: 70.7%; Average loss: 2.7548
Iteration: 2830; Percent complete: 70.8%; Average loss: 2.8041
Iteration: 2831; Percent complete: 70.8%; Average loss: 2.9187
Iteration: 2832; Percent complete: 70.8%; Average loss: 3.0977
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.9075
Iteration: 2834; Percent complete: 70.9%; Average loss: 2.9617
Iteration: 2835; Percent complete: 70.9%; Average loss: 2.8825
Iteration: 2836; Percent complete: 70.9%; Average loss: 2.7901
Iteration: 2837; Percent complete: 70.9%; Average loss: 2.8087
Iteration: 2838; Percent complete: 71.0%; Average loss: 3.0140
Iteration: 2839; Percent complete: 71.0%; Average loss: 2.7554
Iteration: 2840; Percent complete: 71.0%; Average loss: 2.9659
Iteration: 2841; Percent complete: 71.0%; Average loss: 2.9887
Iteration: 2842; Percent complete: 71.0%; Average loss: 2.7072
Iteration: 2843; Percent complete: 71.1%; Average loss: 2.9551
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.7959
Iteration: 2845; Percent complete: 71.1%; Average loss: 2.8003
Iteration: 2846; Percent complete: 71.2%; Average loss: 2.8818
Iteration: 2847; Percent complete: 71.2%; Average loss: 2.7402
Iteration: 2848; Percent complete: 71.2%; Average loss: 2.9429
Iteration: 2849; Percent complete: 71.2%; Average loss: 2.7409
Iteration: 2850; Percent complete: 71.2%; Average loss: 2.9464
Iteration: 2851; Percent complete: 71.3%; Average loss: 2.8051
Iteration: 2852; Percent complete: 71.3%; Average loss: 2.7880
Iteration: 2853; Percent complete: 71.3%; Average loss: 3.1648
Iteration: 2854; Percent complete: 71.4%; Average loss: 2.8878
Iteration: 2855; Percent complete: 71.4%; Average loss: 2.9831
Iteration: 2856; Percent complete: 71.4%; Average loss: 2.9848
Iteration: 2857; Percent complete: 71.4%; Average loss: 2.5254
Iteration: 2858; Percent complete: 71.5%; Average loss: 2.9979
Iteration: 2859; Percent complete: 71.5%; Average loss: 2.8236
Iteration: 2860; Percent complete: 71.5%; Average loss: 2.8150
Iteration: 2861; Percent complete: 71.5%; Average loss: 2.8351
Iteration: 2862; Percent complete: 71.5%; Average loss: 3.0912
Iteration: 2863; Percent complete: 71.6%; Average loss: 3.0995
Iteration: 2864; Percent complete: 71.6%; Average loss: 3.1131
Iteration: 2865; Percent complete: 71.6%; Average loss: 2.8604
Iteration: 2866; Percent complete: 71.7%; Average loss: 2.8978
Iteration: 2867; Percent complete: 71.7%; Average loss: 2.9330
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.8478
Iteration: 2869; Percent complete: 71.7%; Average loss: 2.8726
Iteration: 2870; Percent complete: 71.8%; Average loss: 3.0800
Iteration: 2871; Percent complete: 71.8%; Average loss: 3.0781
Iteration: 2872; Percent complete: 71.8%; Average loss: 3.0751
Iteration: 2873; Percent complete: 71.8%; Average loss: 2.8452
Iteration: 2874; Percent complete: 71.9%; Average loss: 2.8917
Iteration: 2875; Percent complete: 71.9%; Average loss: 2.9300
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.9540
Iteration: 2877; Percent complete: 71.9%; Average loss: 2.9087
Iteration: 2878; Percent complete: 72.0%; Average loss: 2.8519
Iteration: 2879; Percent complete: 72.0%; Average loss: 2.7447
Iteration: 2880; Percent complete: 72.0%; Average loss: 2.6900
Iteration: 2881; Percent complete: 72.0%; Average loss: 2.9299
Iteration: 2882; Percent complete: 72.0%; Average loss: 2.8768
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.6175
Iteration: 2884; Percent complete: 72.1%; Average loss: 3.0957
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.7615
Iteration: 2886; Percent complete: 72.2%; Average loss: 2.9611
Iteration: 2887; Percent complete: 72.2%; Average loss: 2.7401
Iteration: 2888; Percent complete: 72.2%; Average loss: 2.8910
Iteration: 2889; Percent complete: 72.2%; Average loss: 3.0254
Iteration: 2890; Percent complete: 72.2%; Average loss: 3.1596
Iteration: 2891; Percent complete: 72.3%; Average loss: 2.8947
Iteration: 2892; Percent complete: 72.3%; Average loss: 2.9237
Iteration: 2893; Percent complete: 72.3%; Average loss: 2.8532
Iteration: 2894; Percent complete: 72.4%; Average loss: 3.0179
Iteration: 2895; Percent complete: 72.4%; Average loss: 2.9661
Iteration: 2896; Percent complete: 72.4%; Average loss: 2.7320
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.9470
Iteration: 2898; Percent complete: 72.5%; Average loss: 2.8613
Iteration: 2899; Percent complete: 72.5%; Average loss: 3.0049
Iteration: 2900; Percent complete: 72.5%; Average loss: 2.9651
Iteration: 2901; Percent complete: 72.5%; Average loss: 2.9317
Iteration: 2902; Percent complete: 72.5%; Average loss: 2.8560
Iteration: 2903; Percent complete: 72.6%; Average loss: 2.8136
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.8824
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.9752
Iteration: 2906; Percent complete: 72.7%; Average loss: 2.7148
Iteration: 2907; Percent complete: 72.7%; Average loss: 2.7983
Iteration: 2908; Percent complete: 72.7%; Average loss: 3.0607
Iteration: 2909; Percent complete: 72.7%; Average loss: 2.5833
Iteration: 2910; Percent complete: 72.8%; Average loss: 2.9956
Iteration: 2911; Percent complete: 72.8%; Average loss: 3.0516
Iteration: 2912; Percent complete: 72.8%; Average loss: 2.9931
Iteration: 2913; Percent complete: 72.8%; Average loss: 2.9565
Iteration: 2914; Percent complete: 72.9%; Average loss: 3.2372
Iteration: 2915; Percent complete: 72.9%; Average loss: 2.9385
Iteration: 2916; Percent complete: 72.9%; Average loss: 2.6671
Iteration: 2917; Percent complete: 72.9%; Average loss: 2.9480
Iteration: 2918; Percent complete: 73.0%; Average loss: 3.0500
Iteration: 2919; Percent complete: 73.0%; Average loss: 3.0060
Iteration: 2920; Percent complete: 73.0%; Average loss: 3.0711
Iteration: 2921; Percent complete: 73.0%; Average loss: 2.8101
Iteration: 2922; Percent complete: 73.0%; Average loss: 3.1670
Iteration: 2923; Percent complete: 73.1%; Average loss: 2.9243
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.9227
Iteration: 2925; Percent complete: 73.1%; Average loss: 3.1049
Iteration: 2926; Percent complete: 73.2%; Average loss: 2.7293
Iteration: 2927; Percent complete: 73.2%; Average loss: 2.9986
Iteration: 2928; Percent complete: 73.2%; Average loss: 2.9344
Iteration: 2929; Percent complete: 73.2%; Average loss: 2.9156
Iteration: 2930; Percent complete: 73.2%; Average loss: 2.7918
Iteration: 2931; Percent complete: 73.3%; Average loss: 2.9451
Iteration: 2932; Percent complete: 73.3%; Average loss: 3.0436
Iteration: 2933; Percent complete: 73.3%; Average loss: 2.8361
Iteration: 2934; Percent complete: 73.4%; Average loss: 2.7529
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.8475
Iteration: 2936; Percent complete: 73.4%; Average loss: 2.9849
Iteration: 2937; Percent complete: 73.4%; Average loss: 2.7510
Iteration: 2938; Percent complete: 73.5%; Average loss: 3.0012
Iteration: 2939; Percent complete: 73.5%; Average loss: 3.0200
Iteration: 2940; Percent complete: 73.5%; Average loss: 3.0300
Iteration: 2941; Percent complete: 73.5%; Average loss: 2.9527
Iteration: 2942; Percent complete: 73.6%; Average loss: 2.7794
Iteration: 2943; Percent complete: 73.6%; Average loss: 3.0026
Iteration: 2944; Percent complete: 73.6%; Average loss: 2.9170
Iteration: 2945; Percent complete: 73.6%; Average loss: 2.9150
Iteration: 2946; Percent complete: 73.7%; Average loss: 3.0145
Iteration: 2947; Percent complete: 73.7%; Average loss: 2.8158
Iteration: 2948; Percent complete: 73.7%; Average loss: 2.9226
Iteration: 2949; Percent complete: 73.7%; Average loss: 2.9212
Iteration: 2950; Percent complete: 73.8%; Average loss: 3.0005
Iteration: 2951; Percent complete: 73.8%; Average loss: 2.7822
Iteration: 2952; Percent complete: 73.8%; Average loss: 2.8008
Iteration: 2953; Percent complete: 73.8%; Average loss: 3.0325
Iteration: 2954; Percent complete: 73.9%; Average loss: 2.8416
Iteration: 2955; Percent complete: 73.9%; Average loss: 2.8476
Iteration: 2956; Percent complete: 73.9%; Average loss: 2.6863
Iteration: 2957; Percent complete: 73.9%; Average loss: 2.9513
Iteration: 2958; Percent complete: 74.0%; Average loss: 2.9007
Iteration: 2959; Percent complete: 74.0%; Average loss: 3.0381
Iteration: 2960; Percent complete: 74.0%; Average loss: 3.1564
Iteration: 2961; Percent complete: 74.0%; Average loss: 2.8266
Iteration: 2962; Percent complete: 74.1%; Average loss: 2.7858
Iteration: 2963; Percent complete: 74.1%; Average loss: 2.9256
Iteration: 2964; Percent complete: 74.1%; Average loss: 2.8254
Iteration: 2965; Percent complete: 74.1%; Average loss: 2.9686
Iteration: 2966; Percent complete: 74.2%; Average loss: 3.0203
Iteration: 2967; Percent complete: 74.2%; Average loss: 2.8977
Iteration: 2968; Percent complete: 74.2%; Average loss: 3.0057
Iteration: 2969; Percent complete: 74.2%; Average loss: 2.7517
Iteration: 2970; Percent complete: 74.2%; Average loss: 2.9887
Iteration: 2971; Percent complete: 74.3%; Average loss: 2.9152
Iteration: 2972; Percent complete: 74.3%; Average loss: 2.8651
Iteration: 2973; Percent complete: 74.3%; Average loss: 3.0604
Iteration: 2974; Percent complete: 74.4%; Average loss: 2.6196
Iteration: 2975; Percent complete: 74.4%; Average loss: 2.9426
Iteration: 2976; Percent complete: 74.4%; Average loss: 2.8127
Iteration: 2977; Percent complete: 74.4%; Average loss: 2.8519
Iteration: 2978; Percent complete: 74.5%; Average loss: 2.8756
Iteration: 2979; Percent complete: 74.5%; Average loss: 2.9852
Iteration: 2980; Percent complete: 74.5%; Average loss: 2.8663
Iteration: 2981; Percent complete: 74.5%; Average loss: 3.1078
Iteration: 2982; Percent complete: 74.6%; Average loss: 3.0077
Iteration: 2983; Percent complete: 74.6%; Average loss: 2.7635
Iteration: 2984; Percent complete: 74.6%; Average loss: 2.8513
Iteration: 2985; Percent complete: 74.6%; Average loss: 2.9129
Iteration: 2986; Percent complete: 74.7%; Average loss: 2.8547
Iteration: 2987; Percent complete: 74.7%; Average loss: 3.0738
Iteration: 2988; Percent complete: 74.7%; Average loss: 2.6272
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.8422
Iteration: 2990; Percent complete: 74.8%; Average loss: 2.9645
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.7630
Iteration: 2992; Percent complete: 74.8%; Average loss: 2.8962
Iteration: 2993; Percent complete: 74.8%; Average loss: 2.9266
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.7828
Iteration: 2995; Percent complete: 74.9%; Average loss: 2.9895
Iteration: 2996; Percent complete: 74.9%; Average loss: 3.0066
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.8601
Iteration: 2998; Percent complete: 75.0%; Average loss: 3.0763
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.8468
Iteration: 3000; Percent complete: 75.0%; Average loss: 2.8651
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.7267
Iteration: 3002; Percent complete: 75.0%; Average loss: 2.9982
Iteration: 3003; Percent complete: 75.1%; Average loss: 2.8022
Iteration: 3004; Percent complete: 75.1%; Average loss: 2.8106
Iteration: 3005; Percent complete: 75.1%; Average loss: 2.7967
Iteration: 3006; Percent complete: 75.1%; Average loss: 2.7927
Iteration: 3007; Percent complete: 75.2%; Average loss: 3.1310
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.9725
Iteration: 3009; Percent complete: 75.2%; Average loss: 2.8803
Iteration: 3010; Percent complete: 75.2%; Average loss: 2.8378
Iteration: 3011; Percent complete: 75.3%; Average loss: 2.8215
Iteration: 3012; Percent complete: 75.3%; Average loss: 3.0337
Iteration: 3013; Percent complete: 75.3%; Average loss: 2.6925
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.9370
Iteration: 3015; Percent complete: 75.4%; Average loss: 2.8644
Iteration: 3016; Percent complete: 75.4%; Average loss: 2.9794
Iteration: 3017; Percent complete: 75.4%; Average loss: 2.7400
Iteration: 3018; Percent complete: 75.4%; Average loss: 2.8151
Iteration: 3019; Percent complete: 75.5%; Average loss: 2.6897
Iteration: 3020; Percent complete: 75.5%; Average loss: 2.8608
Iteration: 3021; Percent complete: 75.5%; Average loss: 2.9611
Iteration: 3022; Percent complete: 75.5%; Average loss: 2.8445
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.8013
Iteration: 3024; Percent complete: 75.6%; Average loss: 3.0877
Iteration: 3025; Percent complete: 75.6%; Average loss: 2.6812
Iteration: 3026; Percent complete: 75.6%; Average loss: 2.7391
Iteration: 3027; Percent complete: 75.7%; Average loss: 2.7971
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.7754
Iteration: 3029; Percent complete: 75.7%; Average loss: 3.1334
Iteration: 3030; Percent complete: 75.8%; Average loss: 2.7308
Iteration: 3031; Percent complete: 75.8%; Average loss: 2.7991
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.8185
Iteration: 3033; Percent complete: 75.8%; Average loss: 2.9759
Iteration: 3034; Percent complete: 75.8%; Average loss: 2.7222
Iteration: 3035; Percent complete: 75.9%; Average loss: 2.8459
Iteration: 3036; Percent complete: 75.9%; Average loss: 2.7957
Iteration: 3037; Percent complete: 75.9%; Average loss: 2.8045
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.8104
Iteration: 3039; Percent complete: 76.0%; Average loss: 3.0521
Iteration: 3040; Percent complete: 76.0%; Average loss: 2.7416
Iteration: 3041; Percent complete: 76.0%; Average loss: 2.8618
Iteration: 3042; Percent complete: 76.0%; Average loss: 3.0587
Iteration: 3043; Percent complete: 76.1%; Average loss: 2.7601
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.9621
Iteration: 3045; Percent complete: 76.1%; Average loss: 2.7366
Iteration: 3046; Percent complete: 76.1%; Average loss: 2.8038
Iteration: 3047; Percent complete: 76.2%; Average loss: 3.0557
Iteration: 3048; Percent complete: 76.2%; Average loss: 2.7624
Iteration: 3049; Percent complete: 76.2%; Average loss: 3.0427
Iteration: 3050; Percent complete: 76.2%; Average loss: 2.8225
Iteration: 3051; Percent complete: 76.3%; Average loss: 2.8155
Iteration: 3052; Percent complete: 76.3%; Average loss: 2.8021
Iteration: 3053; Percent complete: 76.3%; Average loss: 2.7413
Iteration: 3054; Percent complete: 76.3%; Average loss: 2.7610
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.8100
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.9741
Iteration: 3057; Percent complete: 76.4%; Average loss: 2.9588
Iteration: 3058; Percent complete: 76.4%; Average loss: 2.7904
Iteration: 3059; Percent complete: 76.5%; Average loss: 2.8361
Iteration: 3060; Percent complete: 76.5%; Average loss: 2.7862
Iteration: 3061; Percent complete: 76.5%; Average loss: 2.8654
Iteration: 3062; Percent complete: 76.5%; Average loss: 2.7564
Iteration: 3063; Percent complete: 76.6%; Average loss: 2.7552
Iteration: 3064; Percent complete: 76.6%; Average loss: 2.9986
Iteration: 3065; Percent complete: 76.6%; Average loss: 3.3537
Iteration: 3066; Percent complete: 76.6%; Average loss: 2.8222
Iteration: 3067; Percent complete: 76.7%; Average loss: 3.0126
Iteration: 3068; Percent complete: 76.7%; Average loss: 3.0565
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.9767
Iteration: 3070; Percent complete: 76.8%; Average loss: 2.6539
Iteration: 3071; Percent complete: 76.8%; Average loss: 2.9195
Iteration: 3072; Percent complete: 76.8%; Average loss: 3.0118
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.9100
Iteration: 3074; Percent complete: 76.8%; Average loss: 2.8920
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.8620
Iteration: 3076; Percent complete: 76.9%; Average loss: 3.0110
Iteration: 3077; Percent complete: 76.9%; Average loss: 2.7296
Iteration: 3078; Percent complete: 77.0%; Average loss: 2.5811
Iteration: 3079; Percent complete: 77.0%; Average loss: 2.8460
Iteration: 3080; Percent complete: 77.0%; Average loss: 2.8446
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.8734
Iteration: 3082; Percent complete: 77.0%; Average loss: 3.0241
Iteration: 3083; Percent complete: 77.1%; Average loss: 2.6095
Iteration: 3084; Percent complete: 77.1%; Average loss: 2.6852
Iteration: 3085; Percent complete: 77.1%; Average loss: 2.6152
Iteration: 3086; Percent complete: 77.1%; Average loss: 3.0691
Iteration: 3087; Percent complete: 77.2%; Average loss: 2.7887
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.9975
Iteration: 3089; Percent complete: 77.2%; Average loss: 2.8190
Iteration: 3090; Percent complete: 77.2%; Average loss: 3.1364
Iteration: 3091; Percent complete: 77.3%; Average loss: 2.9823
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.8138
Iteration: 3093; Percent complete: 77.3%; Average loss: 2.9748
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.7699
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.7422
Iteration: 3096; Percent complete: 77.4%; Average loss: 2.8336
Iteration: 3097; Percent complete: 77.4%; Average loss: 2.8394
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.5948
Iteration: 3099; Percent complete: 77.5%; Average loss: 2.6860
Iteration: 3100; Percent complete: 77.5%; Average loss: 2.7211
Iteration: 3101; Percent complete: 77.5%; Average loss: 2.6867
Iteration: 3102; Percent complete: 77.5%; Average loss: 2.8513
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.8988
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.7104
Iteration: 3105; Percent complete: 77.6%; Average loss: 2.7294
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.8770
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.8970
Iteration: 3108; Percent complete: 77.7%; Average loss: 2.6771
Iteration: 3109; Percent complete: 77.7%; Average loss: 2.6450
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.8500
Iteration: 3111; Percent complete: 77.8%; Average loss: 3.0909
Iteration: 3112; Percent complete: 77.8%; Average loss: 2.9235
Iteration: 3113; Percent complete: 77.8%; Average loss: 2.9952
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.7591
Iteration: 3115; Percent complete: 77.9%; Average loss: 3.1148
Iteration: 3116; Percent complete: 77.9%; Average loss: 3.2304
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.7776
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.8193
Iteration: 3119; Percent complete: 78.0%; Average loss: 2.9649
Iteration: 3120; Percent complete: 78.0%; Average loss: 2.9443
Iteration: 3121; Percent complete: 78.0%; Average loss: 2.8319
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.9481
Iteration: 3123; Percent complete: 78.1%; Average loss: 2.7816
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.7726
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.9866
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.9532
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.9853
Iteration: 3128; Percent complete: 78.2%; Average loss: 2.7101
Iteration: 3129; Percent complete: 78.2%; Average loss: 2.9494
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.8902
Iteration: 3131; Percent complete: 78.3%; Average loss: 2.7953
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.7900
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.7174
Iteration: 3134; Percent complete: 78.3%; Average loss: 3.0845
Iteration: 3135; Percent complete: 78.4%; Average loss: 2.7266
Iteration: 3136; Percent complete: 78.4%; Average loss: 2.6358
Iteration: 3137; Percent complete: 78.4%; Average loss: 2.7799
Iteration: 3138; Percent complete: 78.5%; Average loss: 2.8613
Iteration: 3139; Percent complete: 78.5%; Average loss: 2.8474
Iteration: 3140; Percent complete: 78.5%; Average loss: 2.8568
Iteration: 3141; Percent complete: 78.5%; Average loss: 2.7497
Iteration: 3142; Percent complete: 78.5%; Average loss: 2.7763
Iteration: 3143; Percent complete: 78.6%; Average loss: 2.7451
Iteration: 3144; Percent complete: 78.6%; Average loss: 2.8324
Iteration: 3145; Percent complete: 78.6%; Average loss: 3.0112
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.8066
Iteration: 3147; Percent complete: 78.7%; Average loss: 2.8543
Iteration: 3148; Percent complete: 78.7%; Average loss: 2.6886
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.6650
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.9637
Iteration: 3151; Percent complete: 78.8%; Average loss: 2.5821
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.7390
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.7545
Iteration: 3154; Percent complete: 78.8%; Average loss: 3.0055
Iteration: 3155; Percent complete: 78.9%; Average loss: 2.8480
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.9584
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.8701
Iteration: 3158; Percent complete: 79.0%; Average loss: 2.5779
Iteration: 3159; Percent complete: 79.0%; Average loss: 2.9987
Iteration: 3160; Percent complete: 79.0%; Average loss: 3.0092
Iteration: 3161; Percent complete: 79.0%; Average loss: 2.7750
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.6388
Iteration: 3163; Percent complete: 79.1%; Average loss: 2.8880
Iteration: 3164; Percent complete: 79.1%; Average loss: 3.1752
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.7560
Iteration: 3166; Percent complete: 79.1%; Average loss: 2.8755
Iteration: 3167; Percent complete: 79.2%; Average loss: 2.9526
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.9634
Iteration: 3169; Percent complete: 79.2%; Average loss: 2.8096
Iteration: 3170; Percent complete: 79.2%; Average loss: 2.6442
Iteration: 3171; Percent complete: 79.3%; Average loss: 2.7396
Iteration: 3172; Percent complete: 79.3%; Average loss: 3.2038
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.6868
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.7706
Iteration: 3175; Percent complete: 79.4%; Average loss: 2.6598
Iteration: 3176; Percent complete: 79.4%; Average loss: 2.9589
Iteration: 3177; Percent complete: 79.4%; Average loss: 2.7163
Iteration: 3178; Percent complete: 79.5%; Average loss: 2.7249
Iteration: 3179; Percent complete: 79.5%; Average loss: 3.0355
Iteration: 3180; Percent complete: 79.5%; Average loss: 2.7255
Iteration: 3181; Percent complete: 79.5%; Average loss: 2.6890
Iteration: 3182; Percent complete: 79.5%; Average loss: 2.7800
Iteration: 3183; Percent complete: 79.6%; Average loss: 2.7286
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.9246
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.7116
Iteration: 3186; Percent complete: 79.7%; Average loss: 2.8315
Iteration: 3187; Percent complete: 79.7%; Average loss: 3.0359
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.7912
Iteration: 3189; Percent complete: 79.7%; Average loss: 2.9379
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.8283
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.7777
Iteration: 3192; Percent complete: 79.8%; Average loss: 2.7303
Iteration: 3193; Percent complete: 79.8%; Average loss: 3.1004
Iteration: 3194; Percent complete: 79.8%; Average loss: 2.9205
Iteration: 3195; Percent complete: 79.9%; Average loss: 2.7872
Iteration: 3196; Percent complete: 79.9%; Average loss: 2.7419
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.6744
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.8646
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.6273
Iteration: 3200; Percent complete: 80.0%; Average loss: 2.7432
Iteration: 3201; Percent complete: 80.0%; Average loss: 2.8786
Iteration: 3202; Percent complete: 80.0%; Average loss: 3.0396
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.4830
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.5752
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.9355
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.8473
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.8567
Iteration: 3208; Percent complete: 80.2%; Average loss: 2.9745
Iteration: 3209; Percent complete: 80.2%; Average loss: 3.0425
Iteration: 3210; Percent complete: 80.2%; Average loss: 2.6633
Iteration: 3211; Percent complete: 80.3%; Average loss: 2.8710
Iteration: 3212; Percent complete: 80.3%; Average loss: 2.8502
Iteration: 3213; Percent complete: 80.3%; Average loss: 2.7253
Iteration: 3214; Percent complete: 80.3%; Average loss: 3.0381
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.9472
Iteration: 3216; Percent complete: 80.4%; Average loss: 2.8555
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.6065
Iteration: 3218; Percent complete: 80.5%; Average loss: 2.8430
Iteration: 3219; Percent complete: 80.5%; Average loss: 2.9040
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.7468
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.8510
Iteration: 3222; Percent complete: 80.5%; Average loss: 2.7425
Iteration: 3223; Percent complete: 80.6%; Average loss: 2.9845
Iteration: 3224; Percent complete: 80.6%; Average loss: 2.6085
Iteration: 3225; Percent complete: 80.6%; Average loss: 2.8849
Iteration: 3226; Percent complete: 80.7%; Average loss: 2.7083
Iteration: 3227; Percent complete: 80.7%; Average loss: 2.6552
Iteration: 3228; Percent complete: 80.7%; Average loss: 2.7760
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.7994
Iteration: 3230; Percent complete: 80.8%; Average loss: 3.0961
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.9779
Iteration: 3232; Percent complete: 80.8%; Average loss: 2.6463
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.7592
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.9981
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.9829
Iteration: 3236; Percent complete: 80.9%; Average loss: 3.0927
Iteration: 3237; Percent complete: 80.9%; Average loss: 2.9516
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.9707
Iteration: 3239; Percent complete: 81.0%; Average loss: 2.9523
Iteration: 3240; Percent complete: 81.0%; Average loss: 2.7377
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.7627
Iteration: 3242; Percent complete: 81.0%; Average loss: 2.8334
Iteration: 3243; Percent complete: 81.1%; Average loss: 2.7152
Iteration: 3244; Percent complete: 81.1%; Average loss: 2.5711
Iteration: 3245; Percent complete: 81.1%; Average loss: 2.7481
Iteration: 3246; Percent complete: 81.2%; Average loss: 3.0522
Iteration: 3247; Percent complete: 81.2%; Average loss: 2.7270
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.5968
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.7464
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.9490
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.9943
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.7312
Iteration: 3253; Percent complete: 81.3%; Average loss: 2.6942
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.8558
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.8194
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.6701
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.7804
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.8345
Iteration: 3259; Percent complete: 81.5%; Average loss: 2.8321
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.7270
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.7594
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.5565
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.9005
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.7314
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.7137
Iteration: 3266; Percent complete: 81.7%; Average loss: 2.7133
Iteration: 3267; Percent complete: 81.7%; Average loss: 2.6908
Iteration: 3268; Percent complete: 81.7%; Average loss: 2.7543
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.8874
Iteration: 3270; Percent complete: 81.8%; Average loss: 2.9221
Iteration: 3271; Percent complete: 81.8%; Average loss: 3.1111
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.7674
Iteration: 3273; Percent complete: 81.8%; Average loss: 2.8072
Iteration: 3274; Percent complete: 81.8%; Average loss: 2.7365
Iteration: 3275; Percent complete: 81.9%; Average loss: 2.9215
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.5721
Iteration: 3277; Percent complete: 81.9%; Average loss: 2.5865
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.9408
Iteration: 3279; Percent complete: 82.0%; Average loss: 2.6160
Iteration: 3280; Percent complete: 82.0%; Average loss: 2.5812
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.6176
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.7440
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.7421
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.7111
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.9478
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.6479
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.9042
Iteration: 3288; Percent complete: 82.2%; Average loss: 3.0361
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.6422
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.6320
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.6515
Iteration: 3292; Percent complete: 82.3%; Average loss: 2.9463
Iteration: 3293; Percent complete: 82.3%; Average loss: 2.6654
Iteration: 3294; Percent complete: 82.3%; Average loss: 2.7440
Iteration: 3295; Percent complete: 82.4%; Average loss: 2.8516
Iteration: 3296; Percent complete: 82.4%; Average loss: 2.7163
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.7087
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.6904
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.7216
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.9683
Iteration: 3301; Percent complete: 82.5%; Average loss: 2.6199
Iteration: 3302; Percent complete: 82.5%; Average loss: 2.7240
Iteration: 3303; Percent complete: 82.6%; Average loss: 2.9683
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.8776
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.8530
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.9000
Iteration: 3307; Percent complete: 82.7%; Average loss: 2.8868
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.7261
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.8452
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.8001
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.7327
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.7986
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.8416
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.8569
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.7136
Iteration: 3316; Percent complete: 82.9%; Average loss: 2.5020
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.9526
Iteration: 3318; Percent complete: 83.0%; Average loss: 2.8715
Iteration: 3319; Percent complete: 83.0%; Average loss: 2.6801
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.9701
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.7191
Iteration: 3322; Percent complete: 83.0%; Average loss: 2.7560
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.8850
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.9880
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.7804
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.6942
Iteration: 3327; Percent complete: 83.2%; Average loss: 3.0693
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.8677
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.8278
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.5057
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.9112
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.7658
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.9196
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.7900
Iteration: 3335; Percent complete: 83.4%; Average loss: 2.7150
Iteration: 3336; Percent complete: 83.4%; Average loss: 2.6943
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.7079
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.8784
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.8649
Iteration: 3340; Percent complete: 83.5%; Average loss: 2.8852
Iteration: 3341; Percent complete: 83.5%; Average loss: 2.6004
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.6099
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.8079
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.6610
Iteration: 3345; Percent complete: 83.6%; Average loss: 2.6637
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.7975
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.4465
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.7468
Iteration: 3349; Percent complete: 83.7%; Average loss: 3.0378
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.7312
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.7089
Iteration: 3352; Percent complete: 83.8%; Average loss: 2.7017
Iteration: 3353; Percent complete: 83.8%; Average loss: 2.7967
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.9691
Iteration: 3355; Percent complete: 83.9%; Average loss: 2.8201
Iteration: 3356; Percent complete: 83.9%; Average loss: 2.6796
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.7303
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.7648
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.7876
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.6948
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.8716
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.6384
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.6412
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.9586
Iteration: 3365; Percent complete: 84.1%; Average loss: 2.7535
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.6682
Iteration: 3367; Percent complete: 84.2%; Average loss: 2.9822
Iteration: 3368; Percent complete: 84.2%; Average loss: 2.8963
Iteration: 3369; Percent complete: 84.2%; Average loss: 2.6468
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.9538
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.9145
Iteration: 3372; Percent complete: 84.3%; Average loss: 2.8081
Iteration: 3373; Percent complete: 84.3%; Average loss: 2.8193
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.7074
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.7667
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.9272
Iteration: 3377; Percent complete: 84.4%; Average loss: 2.7670
Iteration: 3378; Percent complete: 84.5%; Average loss: 2.7537
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.7977
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.6926
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.7211
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.7096
Iteration: 3383; Percent complete: 84.6%; Average loss: 2.7636
Iteration: 3384; Percent complete: 84.6%; Average loss: 2.7649
Iteration: 3385; Percent complete: 84.6%; Average loss: 2.8550
Iteration: 3386; Percent complete: 84.7%; Average loss: 2.7836
Iteration: 3387; Percent complete: 84.7%; Average loss: 2.8708
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.8966
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.8456
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.7436
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.8498
Iteration: 3392; Percent complete: 84.8%; Average loss: 2.8328
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.6972
Iteration: 3394; Percent complete: 84.9%; Average loss: 2.9230
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.8342
Iteration: 3396; Percent complete: 84.9%; Average loss: 2.4136
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.7120
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.8787
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.8451
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.6844
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.7995
Iteration: 3402; Percent complete: 85.0%; Average loss: 2.7342
Iteration: 3403; Percent complete: 85.1%; Average loss: 2.8598
Iteration: 3404; Percent complete: 85.1%; Average loss: 2.7320
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.7075
Iteration: 3406; Percent complete: 85.2%; Average loss: 2.7945
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.7733
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.5103
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.7765
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.7353
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.8366
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.8286
Iteration: 3413; Percent complete: 85.3%; Average loss: 2.6695
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.9032
Iteration: 3415; Percent complete: 85.4%; Average loss: 2.7754
Iteration: 3416; Percent complete: 85.4%; Average loss: 3.0275
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.6787
Iteration: 3418; Percent complete: 85.5%; Average loss: 2.8721
Iteration: 3419; Percent complete: 85.5%; Average loss: 2.7200
Iteration: 3420; Percent complete: 85.5%; Average loss: 2.6001
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.8359
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.8191
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.6157
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.9797
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.9546
Iteration: 3426; Percent complete: 85.7%; Average loss: 2.8582
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.5043
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.5980
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.9705
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.9691
Iteration: 3431; Percent complete: 85.8%; Average loss: 2.6651
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.8208
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.8998
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.7805
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.6282
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.7139
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.6318
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.7341
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.9148
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.8476
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.7673
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.7812
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.7421
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.6750
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.5987
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.7089
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.7006
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.7761
Iteration: 3449; Percent complete: 86.2%; Average loss: 2.6766
Iteration: 3450; Percent complete: 86.2%; Average loss: 2.6740
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.7732
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.6467
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.8244
Iteration: 3454; Percent complete: 86.4%; Average loss: 2.5879
Iteration: 3455; Percent complete: 86.4%; Average loss: 2.8256
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.5995
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.6106
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.8770
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.7544
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.9762
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.5291
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.7252
Iteration: 3463; Percent complete: 86.6%; Average loss: 2.8615
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.5511
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.8105
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.7212
Iteration: 3467; Percent complete: 86.7%; Average loss: 2.6574
Iteration: 3468; Percent complete: 86.7%; Average loss: 2.7881
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.8407
Iteration: 3470; Percent complete: 86.8%; Average loss: 2.8113
Iteration: 3471; Percent complete: 86.8%; Average loss: 2.6499
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.8099
Iteration: 3473; Percent complete: 86.8%; Average loss: 2.9196
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.6146
Iteration: 3475; Percent complete: 86.9%; Average loss: 2.5448
Iteration: 3476; Percent complete: 86.9%; Average loss: 2.9809
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.8114
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.7967
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.4912
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.5744
Iteration: 3481; Percent complete: 87.0%; Average loss: 2.5533
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.8343
Iteration: 3483; Percent complete: 87.1%; Average loss: 2.6584
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.5541
Iteration: 3485; Percent complete: 87.1%; Average loss: 2.7541
Iteration: 3486; Percent complete: 87.2%; Average loss: 2.7565
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.7149
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.8975
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.8684
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.5933
Iteration: 3491; Percent complete: 87.3%; Average loss: 2.8639
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.9617
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.7147
Iteration: 3494; Percent complete: 87.4%; Average loss: 2.8799
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.7504
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.7384
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.9008
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.8610
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.7277
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.8084
Iteration: 3501; Percent complete: 87.5%; Average loss: 2.7369
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.8622
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.8452
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.8798
Iteration: 3505; Percent complete: 87.6%; Average loss: 2.6825
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.7585
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.6790
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.7436
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.8642
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.7373
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.6635
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.7844
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.6204
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.7363
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.7613
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.6371
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.7361
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.3810
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.8312
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.6680
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.7613
Iteration: 3522; Percent complete: 88.0%; Average loss: 2.9038
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.8982
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.8486
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.8944
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.6533
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.7260
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.7343
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.7273
Iteration: 3530; Percent complete: 88.2%; Average loss: 2.7344
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.5343
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.6391
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.6755
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.6555
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.7791
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.5645
Iteration: 3537; Percent complete: 88.4%; Average loss: 2.8618
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.6001
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.9073
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.7286
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.8236
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.7319
Iteration: 3543; Percent complete: 88.6%; Average loss: 2.8290
Iteration: 3544; Percent complete: 88.6%; Average loss: 2.8795
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.9448
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.5404
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.7909
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.5172
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.5745
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.7349
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.8694
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.6407
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.4762
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.6078
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.5794
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.7040
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.7276
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.7499
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.7686
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.5597
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.8571
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.8039
Iteration: 3563; Percent complete: 89.1%; Average loss: 2.8221
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.5961
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.7566
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.8479
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.5550
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.6340
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.8667
Iteration: 3570; Percent complete: 89.2%; Average loss: 2.4321
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.6950
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.7156
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.8565
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.9192
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.6887
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.5543
Iteration: 3577; Percent complete: 89.4%; Average loss: 2.5134
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.7323
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.4995
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.6311
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.7786
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.5761
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.7827
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.7257
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.9547
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.7660
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.6707
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.9787
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.6710
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.8910
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.8317
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.7189
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.7061
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.7124
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.6197
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.6696
Iteration: 3597; Percent complete: 89.9%; Average loss: 2.6635
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.7314
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.7887
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.6860
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.5444
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.6454
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.6706
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.5610
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.9225
Iteration: 3606; Percent complete: 90.1%; Average loss: 3.0253
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.5555
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.7700
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.9156
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.5516
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.7501
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.6896
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.7943
Iteration: 3614; Percent complete: 90.3%; Average loss: 2.7169
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.5226
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.7719
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.9156
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.8807
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.6261
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.8597
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.9456
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.4415
Iteration: 3623; Percent complete: 90.6%; Average loss: 2.6610
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.7537
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.7381
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.6288
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.8554
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.5646
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.7568
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.7577
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.6121
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.8046
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.9050
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.7776
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.5911
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.6311
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.7317
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.6302
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.6654
Iteration: 3640; Percent complete: 91.0%; Average loss: 3.0065
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.6232
Iteration: 3642; Percent complete: 91.0%; Average loss: 2.7289
Iteration: 3643; Percent complete: 91.1%; Average loss: 2.7644
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.6409
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.8711
Iteration: 3646; Percent complete: 91.1%; Average loss: 2.7508
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.7296
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.5911
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.8010
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.6265
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.6741
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.8020
Iteration: 3653; Percent complete: 91.3%; Average loss: 2.8807
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.8143
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.7293
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.8241
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.7222
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.6772
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.8004
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.5781
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.6173
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.6717
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.6640
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.6129
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.6291
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.8339
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.8411
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.7771
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.6935
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.5433
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.9876
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.7785
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.7420
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.7087
Iteration: 3675; Percent complete: 91.9%; Average loss: 2.7353
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.4725
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.7748
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.5525
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.8416
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.6641
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.6601
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.8852
Iteration: 3683; Percent complete: 92.1%; Average loss: 2.5134
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.6715
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.7510
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.5189
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.8315
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.6144
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.9487
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.4615
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.8075
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.4082
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.8044
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.6529
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.8226
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.6554
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.6110
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.8224
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.4486
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.8793
Iteration: 3701; Percent complete: 92.5%; Average loss: 2.6235
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.7624
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.6186
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.9786
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.5518
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.6145
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.8785
Iteration: 3708; Percent complete: 92.7%; Average loss: 2.8355
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.7031
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.5983
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.5232
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.9123
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.6558
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.4988
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.8455
Iteration: 3716; Percent complete: 92.9%; Average loss: 3.0732
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.7395
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.6703
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.6132
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.6652
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.6778
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.5447
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.6676
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.4655
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.5163
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.7672
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.6444
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.6423
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.5438
Iteration: 3730; Percent complete: 93.2%; Average loss: 2.7925
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.6776
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.6289
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.7194
Iteration: 3734; Percent complete: 93.3%; Average loss: 2.6743
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.7311
Iteration: 3736; Percent complete: 93.4%; Average loss: 2.5231
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.7209
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.6586
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.7018
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.6766
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.7004
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.7339
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.6128
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.4551
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.6525
Iteration: 3746; Percent complete: 93.7%; Average loss: 3.0014
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.7846
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.7503
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.7293
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.7845
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.4846
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.8144
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.5773
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.8409
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.3661
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.8091
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.5233
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.5107
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.5946
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.7741
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.7836
Iteration: 3762; Percent complete: 94.0%; Average loss: 2.5872
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.4014
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.5585
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.7555
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.6996
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.6016
Iteration: 3768; Percent complete: 94.2%; Average loss: 2.6650
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.6992
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.5434
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.4531
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.6525
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.9350
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.3507
Iteration: 3775; Percent complete: 94.4%; Average loss: 2.5951
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.6262
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.7157
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.6079
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.9461
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.7746
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.4462
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.5829
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.7874
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.6148
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.5544
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.7793
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.6965
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.6454
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.6946
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.5949
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.7292
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.8471
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.8699
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.6010
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.7289
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.8899
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.7001
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.6268
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.6314
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.6207
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.5452
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.5323
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.6830
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.6388
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.8515
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.5584
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.7297
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.6706
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.6029
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.7918
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.6335
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.4312
Iteration: 3813; Percent complete: 95.3%; Average loss: 2.5635
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.7835
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.5127
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.5687
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.8006
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.7525
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.7269
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.6103
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.5645
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.4927
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.6912
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.8109
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.5550
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.4608
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.8884
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.7774
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.6840
Iteration: 3830; Percent complete: 95.8%; Average loss: 2.7373
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.7121
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.8338
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.7615
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.6066
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.6025
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.7541
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.4905
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.4457
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.6555
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.5230
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.4673
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.6789
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.7286
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.5457
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.9072
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.4338
Iteration: 3847; Percent complete: 96.2%; Average loss: 2.3845
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.9070
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.4735
Iteration: 3850; Percent complete: 96.2%; Average loss: 2.5743
Iteration: 3851; Percent complete: 96.3%; Average loss: 2.5195
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.7426
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.6343
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.7159
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.5629
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.9671
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.4291
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.5595
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.5905
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.5826
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.5107
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.7560
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.6360
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.6216
Iteration: 3865; Percent complete: 96.6%; Average loss: 2.5363
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.7267
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.9526
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.7028
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.4869
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.4423
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.7136
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.4908
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.7123
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.5967
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.4127
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.7519
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.5970
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.9611
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.7907
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.4909
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.5126
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.6383
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.8022
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.7999
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.5526
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.4214
Iteration: 3887; Percent complete: 97.2%; Average loss: 3.0218
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.5312
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.6200
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.7066
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.6985
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.7185
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.8617
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.6942
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.3169
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.6689
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.5457
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.6023
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.6736
Iteration: 3900; Percent complete: 97.5%; Average loss: 2.6139
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.4675
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.5726
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.5853
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.5984
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.7759
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.6213
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.5884
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.8265
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.6078
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.5691
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.5969
Iteration: 3912; Percent complete: 97.8%; Average loss: 2.5484
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.4288
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.5705
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.6883
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.6862
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.6354
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.8733
Iteration: 3919; Percent complete: 98.0%; Average loss: 2.6519
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.6193
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.6166
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.6669
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.5392
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.5670
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.7603
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.6023
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.5130
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.5469
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.5841
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.5640
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.7829
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.6245
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.7584
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.7840
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.3595
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.5413
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.6816
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.8050
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.6589
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.9521
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.6888
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.6014
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.7178
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.4663
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.4283
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.4639
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.3851
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.9066
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.6242
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.6767
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.6191
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.6175
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.8575
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.6220
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.5779
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.9025
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.6472
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.7251
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.4298
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.6009
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.8116
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.5220
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.7074
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.5292
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.6172
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.8391
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.6939
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.5986
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.4604
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.6162
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.6365
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.6413
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.7578
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.6039
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.7682
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.6837
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.7732
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.7238
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.6615
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.5288
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.5249
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.8301
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.7610
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.3599
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.7820
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.6667
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.6913
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.5241
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.5692
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.6599
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.6137
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.6824
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.5094
Iteration: 3994; Percent complete: 99.9%; Average loss: 2.8631
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.4421
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.6437
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.8631
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.7000
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.7218
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.3771

Run Evaluation

To chat with your model, run the following block.

# Set dropout layers to eval mode
encoder.eval()
decoder.eval()

# Initialize search module
searcher = GreedySearchDecoder(encoder, decoder)

# Begin chatting (uncomment and run the following line to begin)
# evaluateInput(encoder, decoder, searcher, voc)

Conclusion

That’s all for this one, folks. Congratulations, you now know the fundamentals to building a generative chatbot model! If you’re interested, you can try tailoring the chatbot’s behavior by tweaking the model and training parameters and customizing the data that you train the model on.

Check out the other tutorials for more cool deep learning applications in PyTorch!

Total running time of the script: ( 5 minutes 32.683 seconds)

Gallery generated by Sphinx-Gallery

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources